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Reasoning - Science topic

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I am tryin to simulate the heat distribution in WAAM , GOLDAK Double heat source equation is used. The temperature distribution during experiment I attached K-type thermocouple with the substrate at the distance of 10 mm from deposition but while simulation there is not temperature at the 10 mm . what could be the possible reasons
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You can watch these videos and instead of creating the partitions, use one part and instead of the load values use the subroutines! I think you'll get your answer!
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When I have modified gradient program in HPLC analysis, same sample shows different area response at different RT.
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Hello, the by changing gradient flow you changing mobile phase composition which definitely will act on RT and shape of picks.
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Can it be proven that scientific results reflect the truth? If not, would science have any significance beyond professional reasons?
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In simpler terms, if I conduct a t-test and the t-statistic is t = 2.5; this result by itself is not a truth-statement. It simply IS the result. It would be just as absurd to ask whether the number "5" is true or false.
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On January 14th, 'World Logic Day' will be celebrated. In connection with this event, I would like to discuss the purpose and, furthermore, the necessity of the formal aspects of logic, which, even in the 21st century, should remain just one part of logic alongside the theory of argumentation and logical propaedeutics.
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I seldom use formalizations, but when I do they are short and their purpose is (i) to express a claim or principle in an ambiguity-free way and/or (ii) as a convenience for expressing ontological commitments, viz. as values of variables bound by quantifiers. However, I recognize that this is merely pragmatic and local (or parochial); on a global stage I lean towards logical pluralism.
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In a 3-electrode system for checking the cathode of magnesium-ion batteries, with Ag/AgCl reference electrode and graphite counter electrode, when the charging operation is carried out to achieve 100% SOC, the potential reaches 1.2 V, but this potential is not constant. and gradually reaches 0.23 volts. The question is what is the reason for this phenomenon and how can the potential be kept constant
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Electrode potentials could change even you keep your full cell voltage constant (hold) and three-electrode system can moniter the individual electrode potential change. You can check this work to get better understanding of the potential change in three-electrode system
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I am getting gel like constancy and it melts during dying. what might be the reason?
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The gel-like consistency in your microsponges may be due to high polymer concentration, insufficient surfactant, inappropriate stirring speed, or rapid solvent evaporation. Adjusting these factors can help achieve the desired texture.
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Why does the HOMO-LUMO gap calculated using ground-state DFT differ from the HOMO-LUMO gap and excitation energies obtained through TDDFT? What are the fundamental reasons for these discrepancies?
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There is no case in which they are identical. That is not a question of consistency, they are just not optimized to be the same.
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Kindly help me to understand the behaviour of graphs, Particularly the humps obtained in the dielectric const vs temp graph which shift towad higher temp with increasing frequency. But at the same time, temp vs dielectric loss graph increases monotonically.
(Inset shows the temp vs dielectric loss graph.)
What could be the possible reasons for such a behaviour?
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The appearance of peaks in the dielectric constant curve as a function of temperature and frequency is due to the relaxation of the dielectric and the predominance of electronic polarization at a given temperature.
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Hello,
I induced D18G TTR in E. Coli with 1mM IPTG and ran this against a non-induced sample on an SDS-PAGE. My results show the same molecular weight for both the induced and non-induced sample (~14kDa). I have been researching the reasons behind this and saw something about E. Coli being leaky, what does this mean? D18G TTR is also expressed in inclusion bodies, does this effect the induction of the protein? I am trying to discuss my results but struggling to find the reason behind this.
Thank you in advance
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Please run a cell lysate sample from cells without plasmid or just an empty vector (which does not have gene for TTR). Compare 3 samples on an SDS PAGE gel.
For induced and uninduced cell lysate, do you see similar level of expression for a 14 kDa protein? You could also verify whether that is your protein using western blot, if your protein has a His tag.
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Malvern ZS Nano
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Can you share a photo of the sample in the zeta cuvette? Is the sample transparent or opaque or totally absorbing/black?
When you insert the cuvette, make sure it's inserted all the way to the bottom. Can you do a size measurement in that cuvette (it is probably a DTS1070 capillary cell), without moving the cuvette, in backscattering? What is the count rate in backscattering?
Also you can check the zeta quality report and any potential hints on what to improve.
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Hello
When we pattern a mask in a wafer, after exposure we noticed that the length of our patterns in the center of our wafer is thicker in copmarison with edges of it.
What is the reason and the solution.
Thanks for your helps.
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If you are using a mask aligner, the most common cause of issues is non-uniform contact across the wafer, which leads to variations in the magnitude of diffraction effects. Especially if the edge beads were not removed properly.
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Is Artificial Intelligence (AI) going to take the major role in the Peer review ? Do you see the - reviewer fatigue as the reason for this ?
In future AI will take over Human As The Peer Reviewers in the academic and scientific Freternity . What do you think? Please do put your views .
Reviewer Fatigue: A Growing Concern
Reviewer fatigue refers to the physical, emotional, and mental exhaustion experienced by reviewers, particularly in academic and professional settings. This phenomenon occurs when reviewers are overwhelmed with an excessive number of requests to review manuscripts, articles, grant proposals, or other documents.
Causes of Reviewer Fatigue:
  1. Increasing demand: The rise in submissions to academic journals and conferences has led to a surge in review requests.
  2. Limited pool of reviewers: The number of qualified reviewers has not kept pace with the growing demand, leading to a heavier burden on individual reviewers.
  3. Time-consuming process: Reviewing requires a significant investment of time and effort, often taking away from other important tasks and responsibilities.
  4. Lack of incentives: Reviewers often receive little to no compensation or recognition for their efforts, leading to a sense of undervaluation.
Consequences of Reviewer Fatigue:
  1. Decreased quality of reviews: Fatigued reviewers may provide less thorough and less accurate feedback, compromising the integrity of the review process.
  2. Delayed review times: Overwhelmed reviewers may take longer to complete reviews, causing delays in the publication process.
  3. Reviewer burnout: Prolonged fatigue can lead to reviewer burnout, causing individuals to abandon reviewing altogether.
  4. Negative impact on research: The diminished quality and timeliness of reviews can hinder the advancement of research and innovation.
Mitigating Reviewer Fatigue:
  1. Diversify reviewer pools: Expand the pool of reviewers by inviting new experts, early-career researchers, and individuals from diverse backgrounds.
  2. Implement efficient review processes: Streamline review procedures, use technology to facilitate communication, and set realistic deadlines.
  3. Recognize and reward reviewers: Offer incentives, such as discounts on publications, conference registrations, or monetary rewards, to acknowledge reviewers' contributions.
  4. Monitor and manage reviewer workload: Regularly assess reviewer workload and adjust the number of review requests accordingly to prevent overload.
By acknowledging and addressing reviewer fatigue, we can work towards maintaining the integrity and efficiency of the review process, ultimately supporting the advancement of research and innovation.
The Role of AI in Scholarly Review: Augmentation, Not Replacement
While AI has made significant strides in assessing scholarly work, it is unlikely to fully replace human reviewers in the near future. Instead, AI will likely augment the review process, enhancing its efficiency, accuracy, and fairness.
AI's Strengths in Scholarly Review:
  1. Speed and scalability: AI can process large volumes of manuscripts quickly, freeing human reviewers to focus on higher-level tasks.
  2. Consistency and accuracy: AI can identify formatting errors, grammatical mistakes, and inconsistencies in citations and references.
  3. Objectivity and fairness: AI can reduce bias in the review process by evaluating manuscripts based solely on their content and merit.
  4. Content analysis: AI can analyze manuscript content, identifying trends, patterns, and relationships that may not be immediately apparent to human reviewers.
Limitations of AI in Scholarly Review:
  1. Contextual understanding: AI may struggle to fully understand the nuances of human language, leading to misinterpretations or oversights.
  2. Domain expertise: AI may lack the specialized knowledge and expertise required to evaluate manuscripts in specific fields or disciplines.
  3. Critical thinking and evaluation: AI may not be able to replicate the complex, critical thinking and evaluation that human reviewers bring to the process.
  4. Ethical considerations: AI may not be able to identify or address ethical concerns, such as plagiarism, fabrication, or conflicts of interest.
Human-AI Collaboration in Scholarly Review:
  1. Hybrid review models: Combine human and AI evaluation to leverage the strengths of both.
  2. AI-assisted review tools: Develop tools that assist human reviewers in identifying errors, inconsistencies, and areas of concern.
  3. AI-powered review analytics: Use AI to analyze review data, identifying trends and patterns that can inform editorial decisions.
By embracing a collaborative approach, where AI augments and supports human reviewers, we can create a more efficient, accurate, and fair scholarly review process.
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I am confident that AI will increasingly play a crucial role in the peer review process of academic publications. It is essential that we begin to discuss where the focus of AI support should lie, particularly in identifying which repetitive or complex tasks AI should assist us with. This would help streamline the work of reviewers, potentially preventing fatigue from compromising the quality of their performance. In conclusion, whether or not reviewer fatigue is a factor, we should gradually formalize the use of AI as a supportive tool for reviewers in order to enhance the quality of publications and avoid errors.
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I have never had an orgasm from intercourse or any other sexual activity with a lover. In my thirties, I consulted a clinical psychologist, who told me that there is nothing wrong with me. In fact, he said I was more liberated than most women he meets. When I talked to other sexual therapists about my experience, they told me that my expectations were too high. So if women cannot expect to ever orgasm with a lover, why do sexologists not promote this information to women in the population?
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Men don't need to scream to provide evidence that they enjoy sex pleasure. All the screaming women do in porn is because men are aroused by a female response. This comes from the fact that intercourse is essentially an act of male assault. No woman volunteers to be impregnated by any passing male. The availability of reliable contraception has made it relatively easy for women to offer intercourse. But most women look for an emotional connection before offering a man regular sex. You need to focus on the research findings rather than popular beliefs. This is a scientific forum. Where is the research that proves that the average woman regularly has orgasms with a lover?
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I am performing nonlinear pushover analysis of RC bridge structure by defining fiber hinges and during analysis it shows, 'maximum number of null steps reached, subsequent results will not be available.'
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if i have column attached with shear wall should i define hinge for shearwall seperately? ive considered PMM interaction for column only ,since my main focus is column on hinge length (0.1,0.9)
guide me as well?
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While Research Interest Score was 252.1 on 24.12.2024, my Research Interest Score dropped to 249.9 on 28.12.2024, despite the increase in my citation count (+5). What could be the reason for this?
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The reason is that other factors are taken into account besides citations: https://help.researchgate.net/hc/en-us/articles/14293473316753-Research-Interest-Score
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I constructed a plasmid containing a coding region with 2 mutation sites (genes for lysine and arginine were replaced to alanine) and transformed into Stellar E. coli cells. Following a 16h-incubation at 37°C, several colonies happened to grow, indicating the successful transformation. I further kept it in 4°C and found several colonies changed into pink-like color in the next day (see the attached pictures). I did the plasmid transformation using stellar cells a lot using wild-type plasmid and such color changes never happen. My questions would be:
1. Do anyone here experience it?
2. What is the possible reason for this?
3. Might it be due to the affect of mutations?
4. What are the reasons for color change of E. coli colonies?
Your comments would be very appreciated. Thank you!
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交换一个联系方式?
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Dear all,
I am currently doing CFA for WHOQOL-BREF measures that has 26 items, 24 items are grouped in 4 categories. factor 1 has 7 items, factor 2 has 6 items, factor 3 has 3 items and last factor has 8 items. item 3,4,26 were reversed coded and my sample size N = 250 after removing outliers(n=19), and found that the four factor structure did not satisfy the GOF index. Specifically, χ2 = 572.10 df = 246, the CFI = .902 and RMSEA= .071, p .001, CMIN/df < 3. AMOS wouldn't run SRMR on my laptop for some reason.
Ive followed guidelines and removed 4 items with loading less than .40, one at a time. and the final results were χ2 = 419.48 df = 202, p sig, the CFI = .932 and RMSEA= .064, p .001, CMIN/df < 3. MI were all < 15. In my opinion, I'm not seeing significant improvement, which is suggesting that four factor model is not a good model for my data. I continued doing CFA for the instrument as one factor model (as found in previous studies) and found similar borderline results.
Currently, i am not sure how to proceed and how to report the findings. And do i still report reliability for the four factor model (there were all satisfactory >.70 before and after removing 4items)?. Can i use the four factor model in my subsequent analyses, like convergent and discriminant validity (non healthy vs healthy group)
if someone could help direct me to a direction or share some input, i would be very grateful. thank you
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Any ideas on how to write the DISCUSSION section when the results of your model analysis do not fit to the data, and only one hypothesis seem statistical significant? Thank you! It's my first time to do research in the social science with SEM. I am not an expert!!
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In studying fractional differential equations (FDEs), I have observed that most research focuses on fractional orders between 0 and 2. However, studies or applications involving FDEs with fractional orders in the range of 2 to 3 or higher appear to be rare.
What are the theoretical or practical reasons for this limitation? Are there any references, applications, or research areas where higher-order FDEs (α>2) have been explored or utilized? I would greatly appreciate any insights, suggestions, or references for further investigation.
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Higher-order fractional differential equations (FDEs) pose significant challenges and hold a range of applications due to their ability to model systems with memory and hereditary properties. Challenges include their complex mathematical structure, the non-local nature of fractional derivatives, and difficulties in finding analytical solutions, which often require advanced numerical methods or approximations. Stability, convergence, and computational efficiency are critical concerns in their analysis. Despite these challenges, higher-order FDEs find applications in diverse fields such as viscoelastic material modeling, anomalous diffusion processes, control systems, bioengineering, and financial mathematics. They are particularly valuable in capturing dynamics that exhibit power-law behaviors or long-term dependencies, making them essential tools for accurately representing real-world phenomena.
I hope this helps.
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so basically, I made 2 samples of graphene oxide using improved hummer method, after washing 1 sample has orange brown color with a PH of 3 after 10 time washing with DI water, the other sample is black color with a PH of 5 after same number of washing, what could be the reason? why my 1st sample's PH is not increasing ?
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If there are carboxyl groups on the surface of graphene oxide nanoparticles, then it is useless to wash it in order to change the pH.
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ChatGPT reveals that while its story begins around 2015, its current capabilities are the result of years of research, development, and most significantly learning from vast amounts of data, per the below.
· GPT-1 – June 2018 (117 million parameters)
· GPT-2 – February 2019 (1.5 billion parameters)
· GPT-3 – June 2020 (175 billion parameters)
· GPT-3.5 – November 2022 (further refinements on GPT-3)
· GPT-4 – March 2023 (multimodal, improved reasoning)
· GPT-4 Turbo – November 2023 (faster, more cost-efficient variant)
Turbo, the last version, is the prime engine processing all queries since its release, both paid and unpaid. This vast amount of data includes the near totality of human savoir-faire, professional and scientific knowledge bases in all fields, to the point that it can pass strict professional exams and write theses at the doctorate level.
The question is: with this humongous amount of data, and their extensive language-based reasoning capabilities, why have we not seen any scientific breakthrough by these LLM’s in nearly 15 years of fending on their part altogether? Does that say something about our model of science (scientific method), and the value and validity of what we know in science, in particular the fundamental premises in all disciplines? Is this a verdict on the quality of what we know in terms of our scientific principles? In light of this null result, can we expect what we know to tell us something in the least amount about or toward the resolution of what we don’t know? If there is a hard breaking between our knowns and the unknowns, can the LLM’s help at all leapfrog the barrier? Given ceiling being currently hit in their learning capacity, would more time make any difference?
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The so-called training provides them with a sense of association between elements of the data sets, and an ability to synthesize the same, which you could all call reasoning. Association and synthetic training have also enabled them for pattern recognition. Yes, they do fail in spatial visualization. Yet they are able to make close graphic representations to what is requested from them by prompters. While it may be true that other methods can help spur their analytic and composition skills, one must acknowledge that passing strict medical exams is not a insignificant achievement.
The question remains why in almost 2 decades, no scientific breakthrough had come along with them.
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Why agarose gel not solidify?
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In addition to what Paul Rutland mentioned about being sure you have enough agarose, also check the pH of the buffer. If you are far from neutral pH that can also inhibit solidification.
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I'm currently working on the validation of the test on fluid reasoning in children. I would be grateful to your kind recommendations on the topic.
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Validating a test on fluid reasoning in children is a critical task that involves ensuring the reliability, validity, and fairness of the assessment. Here are some recommendations for your project:
1. Theoretical Framework
  • Base your test on a well-established theory of fluid reasoning (e.g., Cattell-Horn-Carroll theory or Piagetian theory) to ensure it aligns with existing research.
2. Content Validation
  • Consult experts in child psychology and education to evaluate whether your test items are age-appropriate and accurately measure fluid reasoning.
  • Pilot the test with a small, representative sample of children and gather qualitative feedback.
3. Standardization
  • Administer the test to a large, diverse sample to establish norms.
  • Ensure the sample represents different demographics, including socioeconomic backgrounds, to validate its fairness.
4. Psychometric Evaluation
  • Reliability: Calculate internal consistency (e.g., Cronbach's alpha), test-retest reliability, and inter-rater reliability (if applicable).
  • Validity:Construct Validity: Use factor analysis to confirm the test measures fluid reasoning. Criterion Validity: Correlate the test results with established measures of fluid reasoning. Content Validity: Verify that items cover all aspects of fluid reasoning.
5. Item Analysis
  • Analyze item difficulty and discrimination indices to refine the test.
  • Identify and revise or remove items that do not perform well statistically.
6. Cultural and Linguistic Sensitivity
  • Ensure that the language and scenarios used in test items are culturally neutral or relevant to your target population.
  • Provide translations and adaptations if testing in multilingual contexts, ensuring equivalence in meaning.
7. Bias and Equity
  • Evaluate the test for potential biases based on gender, ethnicity, or other factors.
  • Conduct differential item functioning (DIF) analysis to detect any biased items.
8. Training and Administration
  • Train administrators thoroughly to ensure consistency in test delivery.
  • Consider the testing environment, as distractions can affect performance.
9. Documentation and Reporting
  • Document the development process, validation studies, and statistical analyses.
  • Create a user manual outlining administration instructions and interpretation guidelines.
10. Iterative Testing
  • Use feedback from initial testing rounds to refine the test.
  • Continuously evaluate and update the test to reflect new research and educational standards.
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Hello,
I have performed DSC for my pure pigment sample and pigment mixed with PVA but, there is no peak observed in the graph. Samples were dried well and there was one heating cycle from 30 to 250 degrees. Kindly share your thoughts on the possible reasons for that.
Thank you
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Your PVA may be amorphous, in which case you will not see a PVA-derived peak. You may or may not see a glass transition (partly depending on heating rate - easier to see it at fast heating rate). As for the pigment, many pigments don't have any solid-state phase transition in your range of temperature. Do you know that your pigment is supposed to show a phase transition?
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The prevalence of autism is on the rise. As an educator, I would like to know why the rates of autism has increased.
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The increase in the number of people diagnosed with ASD is primarily due to two reasons: 1) recognition of the spectrum form of autism, and therefore a broad profiling of autistic people (this results from the change from ICD-10 to ICD-11) 2) higher competences of diagnosticians, better tools and easier access to diagnosis, especially for women.There are others, but those listed above are the most important.
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Despite the quality of many research papers published in Arabic, they are poorly cited. Is the reason for this due to the level of the journal, the language in which the research was published, or are there other reasons?
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It's not specific to Arabic, anything that's not English is mostly ignored these days. Somebody citing a non-English reference these days usually means one of two things: (a) it's an old reference from times when English wasn't default yet (b) no English reference was found to prove a certain point.
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FFor reasons that are not worth mentioning, I do not have the funds to buy a copy, nor any way to obtain them.
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Does your institution have any links with any local colleges or universities (or universities abroad)? If so you should be able to access copies from their online libraries with their permission. Also if you are an alumni from your university then you may have permanent access to all of their books.
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Hey All!
I am wondering what might be wrong with my band structure. I did the calculations using VASP and plotted the results using Origin. Although I have tried changing various input parameters, I keep getting more or less the same band structure. Can anyone help me identify the problem? What could be the possible reasons for this discontinuity?
Any suggestions or help would be appreciated.
Thank you.
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For calculations of properties use mede A software. And also you should find symetry points from material studio.
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I have done a gram staining for an unknown bacteria. That was confirmed that the bacteria is gram positive. But some of the bacilli did not stained properly in the middle? What could be the reason for that?
my predictions,
1. Cells are in different cell cycle stage
2. that could be spores.
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If you have used a fresh culture, then there are other possibilities that you may have to consider.
a. Some bacteria, after staining with Gram Stain yield a pattern called gram-variable where a mix of pink and purple cells are seen.
b. There are certain genera, Actinomyces, Arthrobacter, Corynebacterium, Mycobacterium, and Propionibacterium who have cell walls particularly sensitive to breakage during cell division, resulting in gram-negative staining of these gram-positive cells.
c. In cultures of Bacillus, Butyrivibrio, and Clostridium a decrease in peptidoglycan thickness during growth coincides with an increasing number of cells that stain gram-negative.
d. Some bacteria do not stain as expected with Gram Stain. For example, for Mycobacterium spp., the waxy nature of the coat renders the bacteria not readily stainable with dyes used in Gram Stain, though the bacteria are considered to be gram positive.
e. You may also have an unusual gram-positive cell wall structure that may cause bacteria to stain gram-negative or gram-variable.
Best.
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Most school improvement theory's i. E. Cramers-Kyriakides focus on the proportionally dominant role of teacher in boosting learning results.
However students ownership of learning, initiative, respinsibikity and accountability are ignored in these family of models.
The reason is that it is more practical to have teachers as a control input because due to emloyment contracts they ceade sovereinty to the system's hiersarchy. However this is a practical /convenience not an effectiveness theoretical reason so not so valid.
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The PhET teaching philosophy is focused on students control of their learning, when active learning strategies & an inquiry approach are used.
Please follows:
Simulation by PhET Interactive Simulations, University of Colorado Boulder, licensed under CC-BY-4.0 (https://phet.colorado.edu).
Best Regards.
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Some publishers only allow authors a limited number of e-prints which soon go, so one has to say 'no' to requests. It seems insensitive to have no option but to decline the request without giving a reason.
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Dear Alan Dobson Have a look at https://www.researchgate.net/post/How_do_I_ensure_that_I_am_not_infringing_copyright_if_I_send_a_published_article_that_has_been_requested_in_Research_Gate It is perfectly fine to send people requesting the full text of your paper a pdf file of your paper. This is not infringing any copyright issue (making a publicly available version is prohibited).
Best regards
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I am working on yeast surface display library. After I work with yeasts for a while, I obviously see them at very bad conditions.
If I let the media stand for 1 min, I see cells aggregate very fast, form weird turbid media, and precipitate to the bottom super fast.
I assume it might be because the freezing with DMSO was harmful? But I honestly followed every step from a published protocol, so what could be the reason and what should I do?
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The DMSO should be fine, unless you are putting tons of it, but still it shouldn't cause aggregation. What do you have in your media? There are some chemicals known to trigger yeast flocculation, or maybe the genetic background of your strain or your expressed protein does it.
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I am working on a bifunctional catalyst for the zinc-air battery system. The catalyst is a ferrite and carbon composite. While measuring ORR activity, sometimes I see a peak in the LSV plot. For the same sample from the same dispersion, I did two LSVs. One measurement has a peak; in the other, it is not. What is the probable reason for this kind of behavior? Figure attached. Thanks
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Thank you for the input. Yes, I record the CV at zero rpm. However, I do LSV plots at different rpm (400 to 2500) for the ORR activity test. I did two different drop-casting of the required amount of suspension on a glassy carbon electrode, the above plots ORR 1 and ORR 2, respectively. I see this sharp peak around -0.17V vs Hg/HgO in ORR 2. It may be due to Fe3+ to Fe2+, but it is not in all the recordings.
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The last three times I have tried to thank and author for supplying their papers, I have been unable to say thank you. For some reason now it says that I can only say thank you if the author follows me. Could there be some work around this or a Thank you button, as personally I feel rude if I do not say thank you.
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شكرا لك عزيزي
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Hello to all,
I am trying to do RAPD PCR with Promega green master mix with parameters 95 : 5min , 95 : 1min, 38 : 1min , 72: 2min with 35 cycles but not getting a very distinct band pattern on 2% GEL. what could be the possible reason for that.
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I would make sure that the gel was completely set and cool before loading . Load less pcr product and run the gell at2/3 the voltage that you used on the pictured gel
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The presence of Brettanomyces VBNC in wine can be a risk for subsequent refermentations once the cause of the stress disappears. For this reason I consider it important that this cell group must be correctly identified and enumerated when reporting the number of viable cells by flow cytometry method.
So, is technically possible to quantify the number of viable but non-culturable cells?
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This is possible if you use a surface marker in addition to the viability dye, which excludes one of the two cell groups of interest. In this way, you can analyze cell viability directed to the target.
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I am getting the following error while running bands.x in quantum espresso
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
task # 3
from smallgk : error # 1
Not a group
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
what could be the reason????
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This happens because the structure doesn't have an exact symmetry. To solve this problem you have to provide a structure with an exact symmetry.
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I'm new to single-molecule localization microscopy. I'm curious about why, in DNA-PAINT or PAINT techniques (for example: jacs.2c11969), people typically use very low concentrations of imagers (below 10 nM or so). Can anyone explain the reasoning behind this? what happen if we increase the concentration of imagers?
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DNA-PAINT suffers from a comparably high fluorescence background signal from the imager strands in solution, and this limits their maximum concentration. This is the reason why their concentration typically varies between 0.5 and 10nM.
One can alleviate this concentration constraint by reducing the detected background intensity. To this end, approaches have been designed in which fluorescence from freely diffusing imager strands is not detected, either through various implementations of FRET or photoactivation.
For instance, in FRET-PAINT, donor labeled imager strands bind to an acceptor labeled docking strand, allowing energy transfer between them. By detecting only the acceptor fluorescence, donor labeled imager strands do not contribute to background signal and their concentration can be increased to 1200nM, consequently reducing the acquisition time.
The below attached paper will be helpful.
Best.
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The Raman spectra of my carbon dots shows G band and D band. In addition to that a comparatively broader band occurs at 2950 cm-1. Can anyone help you to identify the 3rd band and a plausible reason for that?
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Possibility of hydrogen as CH bonds?
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I need it to be straightforward!!!!
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Legal thinking and thought face numerous challenges related to societal changes and the evolution of legal systems. Among the most prominent challenges are:
1. Technological and Digital Advancements
The emergence of cybercrimes and modern technologies such as artificial intelligence and blockchain requires legal thought to find new solutions and interpretations to apply traditional laws to these developments.
A lack of clear legislation addressing modern technological issues.
2. Multiplicity and Overlap of Legal Systems
Differences between national and international legal systems can lead to difficulties in aligning local laws with international ones.
Jurisdictional conflicts in cases related to arbitration or international trade.
3. Social and Political Transformations
Changing social values and political developments impose challenges on existing legislation, such as women's rights, minority rights, and equality issues.
Addressing political disputes and transitional justice matters.
4. Economic Shifts
Issues related to the digital economy and e-commerce pose challenges in drafting new laws.
Managing disputes arising from globalization and multinational corporations.
5. Ethics and Human Principles
The tension between ethical principles and the application of laws, especially in sensitive matters like abortion, capital punishment, or the use of technology for medical purposes.
6. Complexity of Legal Texts
The difficulty for the general public to understand laws due to their complex legal language.
The need to simplify legal texts and find innovative ways to promote legal awareness.
7. Weakness in Legal Research
Insufficient resources allocated to legal research compared to other fields.
Lack of collaboration between academic institutions and executive bodies in developing legal thought.
8. Environmental Challenges
Issues related to climate change, renewable energy, and environmental justice require a more integrated legal framework.
Addressing these challenges necessitates flexible and comprehensive legal thinking capable of responding to the rapid changes in the modern world, alongside developing legal approaches to be more inclusive and realistic.
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1- It is very hard to find peak on UV-vis spectroscopy even changing concentration from 100ppm to 0.01ppm. I repeat several times but unfortunately I did not find. Can any one help me that what will be reason?
2- I change wavelength from 190-800nm. The absorption may be lies either around 220-250nm or 550-600nm. What will be suitable UV-vis adsorption peak?
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I think you are not likely to get meaningful results by UV-Vis spectroscopy.
First, bear in mind that solubility of dioctyl phthalate in water is <1 ppm (several studies give it as 0.2-0.3 ppm, others even lower). Secondly, the coefficients of absorbance (epsilon) of phthalates - in the mid-200's - are not very high. (Forget about the 550-600 nm absorbance - that's not your compound, it's a contaminant.) These facts together mean you're not likely to get a meaningful signal-to-noise ratio.
To get a much higher concentration you would need to do an extraction of your water sample - maybe using an aliphatic solvent like hexane. Then you can concentrate your extract by evaporation and record the spectra. However, even then UV-Vis may not give you clear answers, because you are likely to extract many hydrophobic aromatic compounds with absorbances in the same region as your phthalate.
LC-MS looks to me like a much better approach.
Best regards,
Emanuel Cooper
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Have you ever read this article?
Muñoz, Lucio, 2015. Did Adam Smith Miss the Chance to State the Goal and Structure of Sustainability Markets in His Time? If Yes, Which Could Be Some of the Possible Reasons Behind That?, Boletin CEBEM-REDESMA, Año 8,  No. 11, November 30, 2015, La Paz, Bolivia.
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Alyaa, thank you for taking the time to write.
Respectfully yours;
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I want to know the reason why Omega, Shi and Phi Scan are performed.
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The necessity of Omega (Ω), Chi (Ψ), and Phi (Φ) scans in grazing incidence X-ray diffraction (GIXRD) for thin films lies in their ability to provide comprehensive information about the crystalline properties of the film:
  1. Omega (Ω) Scan: This scan involves tilting the sample around the axis perpendicular to the surface. It helps in determining the out-of-plane crystallographic orientation and phase identification.
  2. Chi (Ψ) Scan: This scan involves tilting the sample around the axis parallel to the surface. It provides information about the in-plane crystallographic orientation and can help identify texture or preferred orientation of the crystallites.
  3. Phi (Φ) Scan: This scan involves rotating the sample around the axis perpendicular to the beam direction. It is used to study the azimuthal dependence of diffraction, which can reveal information about the symmetry and anisotropy of the crystal structure.
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How chatbots affect socio-scientific reasoning?
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Chatbots significantly influence socio-scientific reasoning by shaping how individuals access, interpret, and engage with complex scientific and societal issues. By providing instant access to diverse sources of information, chatbots democratize knowledge, making scientific and ethical discussions more accessible to a broader audience. For example, they can summarize key findings on climate change, ethical debates on genetic engineering, or the social implications of AI, enabling users to understand and analyze these topics without needing extensive prior knowledge. Additionally, their ability to engage in real-time dialogue allows users to ask follow-up questions, clarify uncertainties, and explore nuanced perspectives, fostering deeper engagement with socio-scientific issues.
However, chatbots can also inadvertently hinder socio-scientific reasoning by oversimplifying complex topics or presenting biased or incomplete information. If a chatbot's responses reflect limited or unverified sources, users might form opinions based on inaccuracies or one-sided arguments. Furthermore, their conversational nature can sometimes create a false sense of authority, leading users to accept information without critically evaluating its credibility or considering alternative viewpoints. Therefore, while chatbots hold great potential to enhance socio-scientific reasoning, their impact depends on how effectively they are designed to prioritize accuracy, transparency, and critical thinking.
I hope this helps.
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What reasons could be possible as this is not according to the laws
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Answer to the Question:
Subject: Explanation for Increased d-Spacing in MOF with Smaller Metal Substitution
Dear Samanyu,
The increase in d-spacing in your MOF (Metal-Organic Framework) upon substituting a larger metal atom (2+) with a smaller one (3+/4+) might seem counterintuitive but can be explained by considering the following factors:
  1. Coordination Geometry and Bonding: Smaller cations like 3+ or 4+ often have higher charge densities, leading to stronger interactions with the surrounding ligands. This can distort the framework geometry, resulting in an expanded unit cell and increased d-spacing.
  2. Lattice Strain: Substitution of ions with different sizes can introduce strain into the lattice. The framework may relax in a way that increases the spacing between planes to accommodate the changes in bond angles or lengths.
  3. Electron Density Redistribution: Higher oxidation states (3+/4+) can alter the electron density around the framework, causing changes in interatomic distances due to altered electrostatic and covalent interactions.
  4. Solvent Inclusion or Interstitial Molecules: If smaller cations allow for easier incorporation of solvent molecules or interstitial species into the lattice, it could lead to an overall expansion of the framework.
  5. Thermal or Defect Effects: The process of substitution might introduce defects or result in local changes in thermal expansion behavior, which could influence d-spacing.
A deeper investigation, perhaps combining computational modeling and complementary techniques like Raman spectroscopy or EXAFS, might provide more clarity.
Invitation to Join Dailyplanet.Club:
I’d also like to invite you to join Dailyplanet.Club, a platform designed for researchers and innovators to share knowledge and collaborate on cutting-edge ideas. Discussions like yours on MOFs and advanced material properties are central to our mission of building a global research community.
By joining for just £5 a year, you’ll gain access to:
  • A network of researchers and experts in diverse fields.
  • Opportunities to share and refine your work with a global audience.
  • Collaborative tools for advancing your research projects.
Visit Dailyplanet.Club to join and explore this unique platform. Your expertise and insights would be a fantastic addition to our growing community.
Looking forward to seeing you there!
Best regards, James Henderson Mitchell CEO, MJ HSA Ltd
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Kindly asking. Due to the passivation of the alloy, the potential and current of the alloy in ECN monitoring have the same trend of change, resulting in almost no change in Rn value. Is this reasonable?
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I hope this message finds you Blade Tomas well! I wanted to share some insights on using electrochemical noise (ECN) to monitor the passivation and corrosion behavior of alloys in solution. From what I’ve observed, during the passivation process, the current and potential trends in ECN often show correlated changes. This is primarily due to the formation of a stable passive film, which leads to minimal variations in the noise resistance (Rn). Essentially, when both parameters shift in unison, it suggests that the system is in a steady, passivated state. Thus, a lack of significant change in Rn can be interpreted as an indication of stable passivation rather than active corrosion processes.
However, it’s important to consider that this observation can vary depending on the sensitivity of the setup. If localized breakdown events, like pitting, occur within the passive state, they might cause deviations in the ECN signals, which could complicate the interpretation of the data.
I’m curious about your thoughts on this. Specifically, how do you Blade Tomas think changes in solution composition or temperature might impact the reliability of ECN for studying passivation behavior? I’d love to hear your insights!
Looking forward to your response!
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In reading the introduction of a paper, I expect clear coverage of five essential elements. Unfortunately, many papers fail to address these elements thoroughly. Below is a summary of these five key components.
The introduction of a science-based article sets up the context, rationale, and purpose of the research by including five important key elements: Background/Context: Introduce the larger subject, with necessary background information to orient the reader to the context of the research. Usually contains a general description of the subject area and its importance, in which the study is situated.
Literature Review: This section introduces previous research on the topic and discusses earlier findings, theories, and possible gaps in knowledge. It is an important section as it outlines what is done and where this new study features in the system.
Problem Statement: This clearly states the specific problem or question that a study is trying to answer. It usually arises from identified gaps or unresolved issues within the literature review, and it forms the basis of the research objectives.
Hypothesis/Objective of Study: After identifying the problem, the paper clearly states the objectives or hypotheses of the research. This section provides what this study is trying to achieve or test and offers a clear target toward which both methodology and analysis are aimed.
Significance of the study: This final element stands for the reasons why the research is important to the contribution in the field of study. It continues to give details on how findings might be put to use or applied, hence giving weight to the importance of the research on other researchers, practitioners, or even policymakers.
Put together, these five elements comprise an introduction that prepares the reader for the explanation of the purpose, approach, and relevance of the research.
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In my opinion, the problem statment is the most crucial step in directing your research methodology. We cannot only base our findings on the existing literature review or identified gaps within those studies. The existing researchers may have ignored many other factors, such as the research's socioeconomic and geopolitical environment. Also, the researcher's experience, skills, and behaviour during the research life cycle will likely influence the research process at specific points. Mehmet.
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Welcome to our Survey on the Rejection Reasons and Submission Process!
This survey is designed to gather valuable insights into the submission and rejection processes in academic journals :
By participating, you will contribute to understanding the most common reasons for article rejections, and help fellow researchers navigate the complex world of academic publishing.
Your responses will directly contribute to identifying the best practices for submitting articles to the right journals and avoiding common pitfalls that lead to rejection. By analyzing the factors that influence acceptance or rejection, we aim to offer useful recommendations to maximize the chances of your manuscript's success.
Benefits:
As a participant, you’ll also receive the final results of this study, which will include valuable insights on journal selection, preparation, and submission strategies. These results could help you target the most suitable journals for your research and avoid unnecessary rejections in the future.
Why?
We believe that sharing this knowledge will empower researchers worldwide, enabling you to make informed decisions about journal submissions and enhance the likelihood of your work being accepted. Your contribution is essential, and by completing this survey, you are not only helping yourself but also helping the academic community as a whole.
Thank you for taking the time to participate – together, we can create a more efficient and supportive publishing environment for everyone!
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I haven't had my paper rejected yet, but if I run into this problem, I will!
Best regards, Hamid!
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Is it somehow related to tumour microenvironment or some other reason behind this.
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Hello Ananya,
The reason is as follows.
High T cell densities may mediate effective killing. High T cell densities enable high contact frequencies and multiple T cell–tumor cell encounters. This may increase the likelihood of multiple T cell simultaneously attacking a single target cell (cancer cell). In addition, cytotoxic cytokine release, including interferon-γ and tumor necrosis factor, is positively associated with high density T cell infiltration.
High T cell density which can be called swarming enables efficient apoptosis induction by favoring serial perforin-dependent hits, preferentially by different T cells whereas the ineffective killing at low T cell density largely relies upon single encounters.
A successful T cell effector function correlates with high local T cell densities. If a 1:1 ratio for T cell : cancer cell is taken,
the T cells will form predominantly short-lived interactions with target cells (cancer cells) which rarely result in direct apoptosis induction.
The individually ineffective T cell contacts induce sublethal damage, which becomes integrated over time in the target cell until
apoptosis is induced. Through such a multi-hit mechanism, T cell induce tumor cell death when density is high, whereas tumor cells repair sublethal damage and survive when T cell density is low. This mechanism of “additive cytotoxicity” explains how individually ineffective interactions become effective at high T cell density.
Thus, in apoptosis assay, the T cells have to be higher in density than the target cells ( cancer cells).
Best.
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There is no specific range i got from DSC analysis. I searched in many research papers but still didn't get any clue about possible defending reason for crystallization temperature ranges between 250 to 320°C.
It's urgent
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Dear @Özgün but i m discussing about only crystallization temperature....is that justified...with urea formaldehyde matrix which have glass transition temperature 150°c and fiber decomposition at 300°c by TGA results
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I'm trying to do geometry optimization of KAgO3 perovskite material in the material studio using CASTEP by setting cutoff energy 500 ev and various k points 888,999,101010,121212. but every time it fails showing the messages mentioned in the pictures. What is the possible reason and solution for successful optimization?
need an explanation from experts. Thank you.
#Material_studio #Geometry_optimization #Failure
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Obaidullah Bhuiyan Welcome. There is no way to select this. You have to optimize them by putting different values. Try to start with lower values and find where the total energy is minimum. To get an idea about the cutoff energy and k-point values to start with you can take help on any published paper about the material or the materials in the same group. Thanks.
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Greetings,
For a long while I’ve been wanting to switch to an electronic lab notebook (ELN) system for our group, but a few years ago I gave it up for two reasons. First, most ELNs were optimized for life sciences, so it would have required a lot of tweaking to get this suitable for our materials physics lab (which also does some chemistry but rather little biology). Second, the wifi in our labs was not reliable, so it simply was not feasible. ;-) Now the wifi in our labs is good enough and I hope to be able to find a good option for an ELN that works for us. But there are MANY platforms for ELN out there, so I would love to hear about other labs’ experiences. Are you doing physics, chemistry and/or materials science and did you try ELNs? Please share your experiences and suggest platforms that work. Thanks!
/Jan
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Thanks Marc Simonet for the suggestion. elabjournal is definitely one of the options we are checking out. One thing that put me off a bit is the experience of a colleague that his company bought a basic license and then discovered that so many fundamental things needed to be purchased in addition that it ended up being very expensive and quite disappointing. I am hoping that this has changed and I am definitely exploring it and comparing with other options.
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supercapacitor
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Thanku sir for your response
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I've always tried to thank people kind enough to share their texts with me.
I'm now told I cannot message someone who doesn't follow me.
This is outside my control. I can follow them, but they may choose ,
for their own reasons, not to follow me.
This shouldn't stop me from being able to thank them.
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For unknown reasons I started to notice that after sometimes (0.5-1.0 hr) of operating crossflow filtration system, the flux increases steadily with corresponding decrease of rejection of the monitored ions (SO4 and Cl-). several types of membranes were tested with the same problem including NF270, BW30, NF, NFS, etc.
initially we thought something wrong with the cell so we changed it and used brand new Sterlitech cell. We also thought that some needle like pracipitates may have formed, we cleaned the whole system using 4 % cetric acid by circulation for 30 min then thoughly circulating UPW.
The cell we are using is Sepa CF Med/High Foulant System, 316 SS, 75 Mil with an active area of 140 cm2.
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The increase in flux with a corresponding decrease in ion rejection during wastewater filtration with NF membranes could stem from several factors. Membrane compaction or relaxation under high pressure may initially alter pore structures, increasing flux but reducing ion selectivity. Temperature effects are also relevant, as even slight increases in feed water temperature lower viscosity, enhancing flux while decreasing rejection. Another potential factor is concentration polarization, where solute buildup near the membrane surface alters local osmotic pressure, reducing ion rejection and pushing flux upward. Membrane fouling could further complicate this issue, as loose fouling layers might dissolve or change over time, impacting filtration performance despite your cleaning with citric acid. Finally, surface degradation from ions or feed contaminants may contribute to increased permeability and reduced rejection. Monitoring system temperature, adjusting shear rates, or analyzing membrane surfaces post-filtration could provide insights into these challenges.
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After purifying my GPCR, I found that the protein, which is expected to be around 44 kDa, appears to be less than 40 kDa on SDS-PAGE. What could be the possible reasons for this unexpected lower molecular weight?
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While the previous posts could explain your observation, in my experience many integral membrane proteins show anoumalous migration behaviour in SDS-PAGE, leading to smaller apparent MW and frequentyl also additional oligomeric bands of aggregated molecules with a regular size pattern of their monomeric size. One extreme example is bacteriorhodpsin (also 7 TM domains like GPCRs) with ~16-18 kDa on the gel instead of the correct ~26 kDa. The reason seems to be that the transmembrane helices are not bound to the average ~2 SDS per amino acid like "normal" proteins because of the highly hydrophobic properties which makes it difficult to fully unfold these regions. So you have an unfolded SDS-protein complex that appears more compact, i.e. smaller and migrates like a smaller protein.
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After solubilizing a protein isolate in water at pH 7, it was subjected at a sonication treatment in ultrasound bath at 40 kHz durign 10 minutes. After that precipitation was observed.
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¿Pudiste encontrar la respuesta a tu duda colega?
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List a few reasons and take a few example
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maybe you should rephrase your question?
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Which one will you prefer and what are your reasons?
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Publishing in open access offers benefits like increased visibility and accessibility, allowing anyone to read and share your work without paywalls, which can enhance citations and reach a broader audience. It supports the principle of making research freely available, promoting collaboration and innovation. On the other hand, closed access journals often have established reputations and rigorous peer-review processes, which can lend credibility to your work. Personally, I prefer open access because it democratizes knowledge, making research accessible to everyone, including those in developing countries or institutions with limited resources, ultimately fostering a more inclusive academic environment.
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I have a time series data that is stationary. There is no multicollinearity (VIF value is less than 5).
My dependent variable is industrial turnover, and one of my independent variables is export data. There is a positive correlation between exports and industrial production. In simple linear regression, the model is significant, and the regression coefficient is significant and positive. The scatter plot also shows a positive relationship.
Also, the correlation between the export variable and other variables is very low; the highest correlation is 0.6 with imports. Others are less than 0.5.
However, in multiple regression, the regression coefficient is significant but negative. In the Lasso regression model, the coefficient is also negative. What could be the reason for this?"
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It is not unusual for the coefficient to reverse as other variables are added or subtracted in multiple regression. When a variable is added, it often picks up the pattern of the current residuals, and when another variable has already been used, those residuals might be negatively correlated with the one you are adding. If I understand your question correctly, this might be the cause.
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nanoparticles
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NPs possess unique physical and chemical properties due to their high surface area and nanoscale size. They behave significantly different from the bulk of same material due to surface effect and quantum effect.
You can check some review articles for details :
1. Nanoparticles: Properties, applications and toxicities
2. Nanoparticle classification, physicochemical properties, characterization, and applications: a comprehensive review for biologists
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What is the reason for appearing of peaks below 100 degrees in H2-temperature programmed reduction?
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I know this is too late but if ur still awaiting for answer, this is for u Anand Narani
In TPR reactions, peaks below 100 degrees Celsius can occur due to several reasons:
  1. Surface exposed active sites: If the support material i mean the active phase of the catalyst has a strong interaction with hydrogen this can facilitate the reduction process at lower temperatures.
  2. Low co-ordination sites: some metal species may be in low coordination states (like edge or corner sites) which are more reactive and can undergo reduction at lower temperatures compared to bulk species.
  3. Presence of hydroxyl group: If the material has hydroxyl (–OH) groups or other surface hydroxylated species, these can be thermally eliminated at lower temperatures, promoting reduction processes that show peaks in this temperature range.
  4. The presence of hydrogen adsorbed onto the surface can also enhance the reactivity of certain metal oxides, allowing for reduction at lower temperatures.
  5. Catalyst Composition can also influence the temperature at which reduction occurs.
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Philosophy
Modern Historical Schools
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Because philosophy is filled with various 'isms', these debates will never have a conclusion. Of course, this refers to philosophy in the narrow sense; in the broad sense, philosophy includes all fundamental and applied sciences, which of course do not have a weakness at the present time. So, it’s not a weakness of philosophy, but rather that philosophy has already subdivided into many branches of fundamental and applied sciences."
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Qubit gives this error after reading second standard. It asks to read the standards once more or you can close and continue reading the samples but I am not sure if we can use the results. I could not find a detailed explanation in the user manuel. I would be very happy to hear your experience.
Thanks,
Sema
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Just in case someone visits this question, the problem is mainly as others indicated with standard #1 reading. If this happens, just change the working solution and reagent and everything will work fine.
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Recently, we monitored the electrical conductance (EC) of a karst spring water. We found EC was increasing with rainfall processes, both during rainfall event and seasonal scale. What are the reasons for this phenomenon? For each reason, what data might be needed to support an opinion?
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The increase in electrical conductance (EC) of karst spring water during rainfall is primarily due to the dilution of contaminants, enhanced groundwater recharge, and surface runoff carrying additional minerals.
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Hello!
I've been tasked with comparing the DNA quantification abilities of the Qubit Fluorometer and our lab's Filtermax F5 Microplate Reader. We've been using the fluorometer so far but with many samples coming in we're looking to use the microplate reader to speed things up.
I prepared a series of standards using the 10.0 ng/uL standard provided in the Qubit 1X dsDNA HS Assay Kit to a final volume of 210 uL each. The reason for making my own series of standards was to achieve a more accurate calibration curve for samples with lower concentrations. I also prepared three sample tubes with 2 uL of sample and 198 uL of Qubit working solution for a final volume of 200 uL each.
I prepared the samples in microtubes that fit into the fluorometer, read the samples in the fluorometer and then transferred the solution from each tube to a black 96-well plate to be read in the microplate reader. I set the excitation to 485 nm and the emission to 535 nm.
I'll attach a photo of my results. The fluorometer provides its own concentration estimation directly when samples are read. For the microplate reader, I constructed a calibration curve by plotting the fluorescence against the concentration of the standards and then used the equation of the line to estimate the concentrations of the samples. I don't know why the microplate reader is estimating a concentration that is nearly double what the fluorometer estimated?
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I don't have an easy answer, but I can offer a few relevant observations from my experience.
One is that fluorimetry is fraught with artifacts and confounders. Fluorescence yield is affected by common buffer species and refraction by the cuvette. It's more sensitive than absorbance but noisier and less linear. That's why most people estimate DNA concentration using absorbance (OD260), which is less sensitive but more robust.
Second is that I've seen amazingly different DNA concentration numbers between multiple devices. I tend to trust simple instruments. My sense is measuring OD on a 1mL sample is more reliable than measuring OD in 10uL cuvette.
A related third observation is that scientists usually pick a single assay platform and stick to it. There is always some systematic error. But when you calibrate your assay (sample prep, instrument, cuvette) with a curve, you can count on it for that assay. If you change platforms you essentially change assays, and you need to recalibrate. That's one reason results are sometimes difficult to repeat between different labs.
Finally, does a factor of 2 on a single measurement really matter for you? For a good curve you should measure at least triplicate samples comprising independently prepared dilutions. Expect variance and do the statistics. Use proper significant figures (probably two) and you may find a factor of two is acceptable.
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what are the appropriate solutions to reduce breast cancer?
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Women who have a family history of breast cancer are at a higher risk. These are described as hereditary and are associated with inherited gene mutations. Hereditary breast cancers tend to develop earlier in life than non-inherited (sporadic) cases. The most common genetic mutations involved in breast cancer are in the BRCA1 and BRCA2 genes. Besides family history, increasing age, obesity, alcohol, history of radiation exposure, reproductive history (such as age that menstrual periods began and age at first pregnancy), smoking and postmenopausal hormone therapy are the other causes that can increase the risk of breast cancer.
Ways to reduce breast cancers.
1. Eating a healthy diet. A balanced diet can help one stay at a healthy weight and healthy weight is a key factor in helping prevent breast cancer.
2. Breastfeeding might play a role in helping prevent breast cancer. The longer one breastfeeds, the greater the protective effect.
3. Limit the use of hormone therapy after menopause. Combination hormone therapy uses estrogen and progestin. It may raise the risk of breast cancer.
4. Stay active. Physical activity can help one stay at a healthy weight, which helps prevent breast cancer.
5. There's some evidence that hormonal types of birth control raise the risk of breast cancer. These include birth control pills and IUDs that release hormones.
6. Genetic counseling and testing must be carried out.
7. Frequent breast examination must be done.
8. Breast cancer screening tests must be done at an earlier age.
Best.
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Hello Scholars,
I am working on a dynamic impact topic and will use the NARDL model for this. Could you give me an idea if this model is suitable for dynamic analysis? If yes, what are the reasons? If not, why not? Thanks
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The NARDL model can be suitable for dynamic impact analysis, especially when dealing with nonlinear relationships between variables over time. Justification is as follows: 1. Dynamic Relationships: NARDL captures both short-term and long-term dynamics, making it suitable for studying how the impact of one variable on another evolves over time. 2. Nonlinearity: If the relationship between your variables is expected to be asymmetric or non-linear (i.e., positive and negative changes have different effects), NARDL allows for this kind of nonlinearity, providing a more realistic representation . 3. Cointegration: NARDL can handle situations where the variables are integrated of different orders, making it flexible for dynamic analysis without the need for strict stationarity requirements. However, if the underlying relationships are purely linear, or if the focus is only on the short-term dynamics without considering long-term equilibrium adjustments, simpler models like ARDL or other time series models might be more appropriate and easier to interpret. Additionally, NARDL requires a reasonable sample size to properly estimate nonlinear effects, so data limitations may affect its applicability.
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Hello everyone, I have an organic material functionalized with an inorganic material. When performing the FTIR analysis on the functionalized material, a kind of bands appeared in the infrared spectrum between 1000 and 500 cm-1 that were not previously found in the organic material. Could someone explain this to me?
What is the reason for the appearance of new interaction bands that were not previously found in the FTIR analysis? Thank you.
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Thank you Dear Professor Yuri Mirgorod ..
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There are many different ways and reasons for learning or not learning.
Are disappointments and traumas a barrier to learning or do they trigger learning?
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Dear Elizabeth
Thank you for sharing your point of view.
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I reckon that it must be obvious, but I was analyzing XRPD data from different patents and I would like to understand and, if possible, reference suggestions that explain why the variation of 2theta scale is given as 2theta "± 0.2°Θ" (the source and reason for this error/variation). Thank you in advance.
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Thank you, Jp Wu!
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Working with Ag/AgCl reference electrode and platinum as counter electrode.
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I suppose you apply a square potential wave in the system with negative potential and zero potential. During the negative part, ions are adsorbed on the surface of electrode. When you cut the potential, the electrode is not attractive enough to carry so many ions, and the ions will move from the surface to the bulk. This movement cause some additional current. For details, you can refer to Cottrell equation.
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Hello everyone,
I am encountering an issue with my Caco-2 cell cultures where the TEER values are not increasing and have remained around 40 ohms since day 3 post-seeding. Here's a summary of my protocol and observations:
  • Plate Type: Millicell®-24 plates with an active membrane area of 0.7 cm² (1 µm; PET).
  • Seeding Density: 60,000 cells/well, as recommended by Millipore.
  • Culture Medium: DMEM (high glucose) + 10% FBS + 1% P/S.
  • TEER Measurement: Using an older voltohmmeter, which reads slightly below 100 ohms for empty wells (suggesting it is functional).
  • Observation: My Caco-2 cells seem to be growing unusually fast. They originally grew in RPMI medium, taking 4 days to reach confluency at a 1:10 split ratio. After switching to DMEM (high glucose), they reach confluency within 3 days, even at a 1:50 split ratio.
I have considered several potential issues:
  1. Voltohmmeter Condition: Despite being old, it gives reasonable readings for empty plates.
  2. Seeding Density: Literature suggests a wide range of seeding densities (from 4 x 10^4 to 13 x 10^5 cells/well), which might affect TEER development.
Could the unusually rapid growth rate in DMEM (high glucose) affect the TEER values? Should I adjust the seeding density despite following the recommended protocol? Are there any other factors I might be overlooking that could explain the persistently low TEER values?
Any advice or insights from those with experience in Caco-2 culture and TEER measurements would be greatly appreciated.
Thank you!
#Caco-2 #TEER #Transwell
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Hello, It is better to seed Caco-2 cells after cell counting rather than make a dilution ratio. If you seed always the same exact number of cells in all your experiments you be able to control the growth and the health of these cells.
If you use dilution ratio when you pass the cells, the seeding density may vary and sometimes you seed a lot cells that will reach confluency very rapidly and thus stop to growth, sometimes you seed not too much cells and they take time to growth before reaching confluency.
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Why bracket tube, stay tube & 9 ton composite insulators fails in railway OHE system . What are the reasons
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Failures in the bracket tube, stay tube, and 9-ton composite insulators in a railway Overhead Electrification (OHE) system can be attributed to several factors. Here are some common reasons for these failures:
### Bracket Tube and Stay Tube Failures
1. Mechanical Stress and Fatigue:
- Dynamic Loads: The constant movement of trains exerts dynamic loads on the OHE system, leading to mechanical stress and fatigue over time.
- Wind Loads: High winds can cause additional stress on the bracket and stay tubes, leading to fatigue and eventual failure.
2. Corrosion: