DRIFT - Science topic

Richard Lewis said "Can you summarise how you arrived at this result."

Yes, since electric potential is energy per charge one can not simply count the number of electrons and number of protons, this may give you the average charge, but not the average electric potential.

Remember we are talking about E/q and protons have substantially more energy than electrons 938 MeV vs 511 keV so the energy per charge of ground potential is on the order of Fe56 (iron) which makes sense because earth is for the most part iron.

Another way to describe energy per charge is "mass per nucleon" and for Fe56 tis happens to be close to 930 MeV/c^2.

Hydrogens mass per nucleon is 938 MeV/c^2 and as we know there are no elements above Hydrogen in the table of elements, so unless someone comes up with a super element that has a mass per nucleon higher than hydrogen I think it is safe to assume 938 MeV to be the maximum energy per charge ratio in our local universe.

So it seems perfectly reasonable to think of the absolute electric potential scale as going from zero to 938 million volts, where the electron sits at 0.5MV, earth sits at 930 MV and the proton sits at 938MV.

Now do you see why it is so absurd not to consider the observer as having an effect on experiments?

We are literally the elephant 🐘 in the room.

Fiber-optic temperature sensors operate on the absorption/ transmission properties of gallium arsenide crystal semiconductors. Increases in the crystal's temperature have the effect of shifting its transmission spectrum to higher wavelengths, jumping from essentially 0% to 100% at a specific wavelength.

Besides filtering, cheking osmolarity and ion composition of your patch solution, you may try perforation with amphotericin as an option for WCR.

In my opinion, I can see the potential impact of AI-powered text generation in those areas. For instance, ChatGPT or other similar AI systems have the potential to revolutionize several areas of society, including the field of research. They can be used to collate and summarize large amounts of information, provide expert advice, and even generate text, making them a powerful tool for researchers. However, there are also other considerations to take into account.

In terms of ethics, there are concerns about the potential for AI-generated text to be used for malicious purposes, such as impersonation or spreading misinformation. The use of AI-generated text also raises questions about authorship and accountability.

The process or craft of research may be impacted by the ease and speed at which AI can generate text, potentially leading to a decrease in the value placed on originality and creativity in research. It may also make it easier for researchers to quickly produce large quantities of text, potentially leading to a decrease in the quality of research.

Academic institution processes may also be impacted. One potential impact is on academic integrity, as the technology may make it easier for students to plagiarize or cheat on assignments. Additionally, these systems may pose a challenge to the detection of plagiarism and academic misconduct, as they are able to generate unique and original text. Educators may need to adapt their methods for detecting and preventing plagiarism and may need to use technology tools to help them identify AI-generated text. Other areas where AI may impact academic institutions include the automation of repetitive tasks and resource management, which can help educators to focus more on teaching and learning.

The ownership and monetization of academic research may also be impacted. In fact, AI-generated text is making it easier for researchers to produce and publish large quantities of research. Therefore, leading to a decrease in the value placed on original research.

Finally, the drift of attention from academia is moving to what AI best facilitates. As AI-generated text becomes more sophisticated, it may become more useful. Specifically, useful for certain types of research, leading to a shift in the focus of academic research.

It is possible that the pH of your solution is increasing due to the addition of the char. Char is a porous, carbon-based material that can absorb and release ions, including protons (H+), which can affect the pH of the solution.

One potential cause of the pH increase could be the release of alkaline ions from the char during the drift experiment. It is also possible that the pH increase could be due to the release of CO2 from the char as it reacts with the water.

To determine the cause of the pH increase, it may be helpful to measure the pH of the solution before and after adding the char, as well as the pH of the char itself. It may also be helpful to try using a lower concentration of NaCl in your solution, or to try using a different type of buffer to maintain the pH at a constant value.

It is also important to ensure that you are using accurate and properly calibrated pH measurement equipment, as incorrect pH readings can affect the results of your experiment.

Hi Ariadna,

I recently met similar questions and found this post. I was wondering if your questions have been solved.

Thank you!

Best,

Yidian

Yes, if I(1) is stationary then I(2) will be stationary.

Dear Moein,

First of all note that you need to work on the envelope of the hysteresis curves.

Moreover, you require many more than one analyzed case. In fact, backbone curves are generalized force-displacement curves portraying the whole behavior of system from loading initiation until collapse, and should included the material as well as geometrical parameters as variables.

You can use either of numerical (soft computing) or calibrated analytical approaches.

For further information, you can call me whenever you wish.

Our images are single color images nd it is good to see in grey color. I think gray color keogram is better visualize the events.

Damages in the structures are the result of distress due to bending, shear, bond, torsion, fatigue or any such action. The resulting phenomenon will be cracks and deformation which may become so significant that the stricture may not be serviceable. collapse in the structure is with large deformation which may not be recoverable. In fact in limit state design a structure will reach a collapse stage when it will have plastic hinges equal to its indeterminacy plus one that is becomes collapse mechanism (due to static mechanism or kinematic mechanism).

I think the main part here is mobile ions (Na+) gettering.

Hi Dr Christian Zorn Thank you for your explanation!

Here is a picture for the situation

It looks like something is wrong with your transfer setup. All the marker dyes are being pulled to the same point in the top middle of your gel. Something there acts as a stronger cathode than the rest of the cathode during your transfer. Perhaps the insulation on one of the wires has degraded.

Baseline drift is a common problem in UV-vis spectroscopy - see https://ibsen.com/resources/detector-resources/subtracting-dark-spectra/ for a useful outline of the issue. I am glad that you were able to pinpoint the source of error in your experiment, Zaid Assaf. Increases in dark current (e.g. due to heating) seem to be a frequent offender, though loss of minor absorbing species (e.g. consumption of acid during a reaction) or changes in the solvent environment (e.g. in gradient chromatography) could also contribute.

My advice to William Letter is to read questions carefully before jumping to conclusions regarding the poster's knowledge and experience level. Condescending comments like "This is basic spectrophotometry" are not constructive and may deter other researchers from seeking assistance online. In this instance, you mistook the poster's issue with baseline drift as a misunderstanding of the Beer-Lambert law, despite repeated clarification. I hope that you will take greater care in your future replies.

Hi! This artefact may be caused by electrode impedances as was commentted before. Another problem that can cause this artefact is the sweat. To eliminate the artefact in EEG already registered, you can filter the low frequencies. Depending on what you need, the filter can be modified.

Best regards

Since the beginning of the coronavirus disease 2019 (COVID-19) pandemic, millions of human cases have been reported globally (WHO 2020). COVID-19 is caused by the newly emerged severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) (Li et al. 2020). By December 2020, >150 vaccine candidates are under development, and several vaccines have concluded phase III trials and licensed in several countries (Corum et al. 2020). Most of the COVID-19 vaccine candidates are mRNA-, subunit-, or vector-based vaccines encoding the spike (S) protein (Folegatti et al. 2020; Jackson et al. 2020; Mulligan et al. 2020; Zhu et al. 2020), which is the surface protein employed by coronaviruses for binding to and entry into the host cell. Thus, SARS-CoV-2 evolution engendering changes of the S protein can have an impact on long-term usability of COVID-19 vaccines.

Within the viral family Coronaviridae, viruses infecting humans belong to the genera Alpha- and Betacoronavirus. SARS-CoV-2 belongs to the SARS-related coronavirus species within the genus Betacoronavirus (Gorbalenya et al. 2020). All coronaviruses share an unusually long single-stranded RNA genome encompassing 27–32 kb and a similar genomic structure (Su et al. 2016). Error-prone RNA-dependent RNA polymerases (RdRp) of RNA viruses such as SARS-CoV-2 contribute to short generation times and high mutation rates (Domingo 1997; Duffy et al. 2008). However, differently from other RNA viruses, RdRp-driven mutation in coronaviruses is limited by a virus-encoded proofreading protein termed nsp14 (Denison et al. 2011).

Beyond the recently emerged SARS-CoV-2 and MERS-CoV, which emerged in 2012/2013 and causes zoonotic infections predominantly on the Arabian Peninsula, there are four endemic human coronaviruses (HCoV). HCoV-229E and HCoV-OC43 were identified already in the mid-1960s, whereas HCoV-NL63 and HCoV-HKU1 were identified more recently in 2004 and 2005 due to increased screening for HCoV in the aftermath of the SARS epidemic during 2003–2004 (Corman et al. 2018). The endemic HCoV and SARS-CoV-2 share several epidemiological and ecological traits. First, the endemic HCoV are comparable to SARS-CoV-2 in their high transmissibility and worldwide spread (Owusu et al. 2014; Corman et al. 2018; Goes et al. 2020). HCoV cause about 10 per cent of all common colds globally, predominantly during fall and winter seasons, and afford seroprevalence rates of up to 90 per cent already in young children (Corman et al. 2018; Edrige et al. 2020). Second, alike SARS-CoV-2 and many other respiratory viruses, repeated upper respiratory tract infections with HCoV are possible despite prior exposure and detectable systemic immune responses (Callow et al. 1990; Farag et al. 2015), as was exemplified by HCoV-229E re-infection within one year in an experimental infection study in humans (Callow et al. 1990). Third, alike SARS-CoV-2 that has likely evolutionary origins in bats, the endemic HCoV originated from an animal source, including bats, rodents and intermediate hosts (Vijgen et al. 2006; Pfefferle et al. 2009; Huynh et al. 2012; Corman et al. 2015; Jo et al., 2020).

Vaccines against COVID-19 provide a powerful mean to create herd immunity and control the pandemic, albeit long-term efficacy remains to be determined (Krammer 2020). No vaccine has yet been approved against any other HCoV. On the contrary, there are several vaccines against the major respiratory illness influenza, which is likely comparable to COVID-19 in the potential for pandemic spread and disease severity (Petersen et al. 2020). Influenza A viruses (IAV) have high evolutionary rates (Fitch et al. 1991) and evolve into antigenically distinct variants escaping community protective immunity within a few years, a process that is termed antigenic drift and requires evaluation and sometimes exchange of vaccine strains on an annual basis (Carrat and Flahault 2007; Rambaut et al. 2008). Among IAV, the endemic subtype H3N2 exhibits the strongest antigenic drift (Fitch et al. 1991; Bedford et al. 2015), largely influenced by its higher mutation rate (Pauly et al. 2017) and large effective population size (Rambaut et al. 2008). Once sufficient community immune responses against SARS-CoV-2 have been built either by wild-type infection or vaccination, a plausible post-pandemic scenario would be that the future trajectory of SARS-CoV-2 will be reminiscent of endemic HCoV and IAV (Petersen et al. 2020).

A few more things to clarify what Ms. Krol said. All of which can impact how your spectrum appears.

1. ATR is a surface technique so if there is something on the surface of your powders it will show up more.

2. The shifts you see are too much to be a difference related to the ATR element. You might expect a few wavenumber shift but not hundreds. see Milosevic, M., Internal Reflection and ATR Spectroscopy. John Wiley & Sons, Inc.: 2012. or 1. Harrick, N. J., Internal Reflection Spectroscopy. 1 ed.; Wiley Interscience: 1967; p 327.

3. For ATR you typically do not use something that has no absorbance as the reference. You just use the clean ATR with no sample.

4. In diffuse reflectance the KBr is used as a diluent and the only reason for using it as a background scan is to "match' particle size and packing. Other diffuse reflectors with no spectral contribution could be used.

5. In the spectra of SiO2 the spectrum does not make sense. SiO2 should show a strong Si-O vibration and that occurs at ~1100cm-1 indicating that the ATR is the correct spectrum. Google SiO2 spectrum and you will get a number of correct spectra illustrating this point.

6. Similar comments for Al2O3. Al-O vibrations are at very low frequencies (typically below 500 cm-1)

7. Although the band positions appear to be about right for the ATR spectra, in addition to the poorly rationed phonon band from the diamond, there are peaks in both spectra that should not be there and might indicate contamination somewhere.

8. Finally - the Praying mantis accessory is a pain to align to properly. Although the spectra look real, you might be getting a spectrum of something you don't think you are.

Summary ATR and diffuse reflectance should not look that different if you are doing the experiments properly. You will see slight peak shifts and slight intensity variations if you have ATR corrected, plotted the diffuse reflectance as KM units etc. I would very strongly recommend going back and starting again.

On the Pike Technologies website (www.piketech.com) you can find a number of resources to understand where you might have gone wrong.

Please refer the paper mentioned below which will give you the latest information:

Levintal, Elad, Yonatan Ganot, Gail Taylor, Peter Freer-Smith, Kosana Suvocarev, and Helen E. Dahlke. "An underground, wireless, open-source, low-cost system for monitoring oxygen, temperature, and soil moisture." SOIL 8, no. 1 (2022): 85-97.

Maybe TOXCHEM model may be of use to you

If you are having a problem with static (which sounds likely given the materials you mention) I highly recommend using an anti-static gun, they are commonly used in analytical chemistry labs. It helps a lot when you are weighing very small amounts or polymers that build up charge easily.

The solution is to spray when the wind is still, increase the volume of the spray solution and use the shield around the nozzle and bring it closer to the surface of the soil.

Check https://www.originlab.com/doc/Quick-Help/find-area-under-curve

Antigenic drift refers to the accumulation of genetic mutations that cause an alteration in the surface of the virus. This is one of the main reasons why a novel flu vaccine is required every year. Antigenic shift occurs when segments from the genome of two different viruses combine to make a novel strain. Coronaviruses are not prone to undergo antigenic drift or shift

kindly see:

Structural Commentaries (User’s Guide – NBC 2015: Part 4 of Division B) (canada.ca)

Hi Jonathan, it is quite difficult to determine which is the cause of the difference in score by just reading the commands you used. I think that you should try to contact with the authors.

best,

Santiago

Dear Farshid,

It is very important to observe the local buckling in the model of the type of analysis and meshing. Therefore, select the type of analysis, RIKS analysis, and create a initial imperfection in the model. Also use the shell element (S4R) for meshing.

The NZSEE document has been effectively superseded by the new Technical Guidelines for Engineering Assessments, which Stelios has provided the link to (also available here http://www.eq-assess.org.nz/).

Some further useful information may be available here:

Raghu Ram Katreddi Thanks Raghu. Already very helpful.

Cheers.

Thanks for this reply. This confirms that the theory of NASA is not fully in accord with geomagnetism.

Dear David Borth

These links might be useful, have a look:

1. https://www.diva-portal.org/smash/get/diva2:743775/FULLTEXT01.pdf

2. https://www.ri.cmu.edu/pub_files/pub4/ang_wei_tech_2004_2/ang_wei_tech_2004_2.pdf

3. https://etd.ohiolink.edu/apexprod/rws_etd/send_file/send?accession=akron1479221542995875&disposition=inline

Kind Regards

Philip Larese-Casanova Drift correction procedures are the ones i have shared the links here with and am sure you will like them and connect them as well

In this Link you can see that the procedure is introduced that can mitigate the deleterious effect of low-frequency noisesoften termed driftson the precision of an analytical experiment.

Hi Amr, I agree with the others. And sometimes, I find it is very practical to visualize the two spots (fixed and delay lines) with a camera at a distance - the further it is located from the fixed and delay arms, the better you see the mis-alignement. This simple camera system is very handy for your alignment.

Good luck there

Best wishes

Hi Somnath Sengupta ,

sorry, I don't know any articles discussing the effect of a variation of the Cs and Ls. A short discussion of the processes in the circuit can be found in Ref. 26 of the article you mentioned but without explanation of the choice of the actual values.

If I understand correctly, you are not interested in modifications of the DSRD but in the circuit. Therefore, a simulator based on SPICE parameters would be sufficient. But seemingly no one has published satisfactory SPICE parameters for DSRDs (possibly, it's due to inherent limitations of SPICE models).

Hence, the usual approach seems to be to model the physical structure of the DSRD by software like Synopsys "Sentaurus" device, and to simulate the circuit in a mixed mode using additionally a SPICE solver. A short description can be found here:

If you don't have access to similar software, if your budget is limited but your time is not (so much), and since the circuit consists of only 10 elements, I guess a viable solution is to write your own circuit simulator (based on simple laws and short time steps), using ideal Rs, Cs, and Ls, replacing the MOSFET by an ideal switch, and modeling the DSRD according to the literature. Once your results for the original circuit look like the published ones, you can start varying the elements.

If I understand, you are saying there is general background signal that is slowly bleaching? I've encountered fluorescence in medium that bleaches with illumination (and then recovers due to diffusion). Riboflavin is one culprit that is in culture medium and is fluorescent. I was told it can even stick to glass.

Hello Qun Zhou ,

I guess you have Zero Check switched on.

A possibility to circumscribe the cause of the drift is to use the preamp out signal available at the rear panel (see page 11-12 of the manual): With External Feedback disabled, you could measure the preamp out signal and see if it is drifting. If not, the cause has to be located behind the preamp.

If the preamp is drifting, you could enable the External Feedback, and use a simple resistor in the external feedback loop. If the drifting disappears something might be wrong with the internal feedback.

Dear Hoshang Hayder

Due to the difference in the response modification factor, the base shear of both structures will not be the same.

Moment-resisting frames are more flexible than the shear wall; don't you think this is one of the reasons for the difference in the amount of allowable drift in these two systems?

Also, drift computations include the fundamental period, scaling modal drift obtained from modal response spectrum analysis, the seismic design base shear, torsional irregularities in structures, and the significance of P-delta effects.

There are a couple plugins, StackReg and TurboReg that should help. Just google it and you can find the files to download and install. They used to come with the Fiji version of imageJ, but looks like you have to get them manually now.

Thermal drift is used to define any evolution of the displacement of the tip while maintaining a constant force. Of course, for time dependent materials, such as polymers, it can be tricky to separate the effect of creep from the effect of thermal drifts (that are cause by slight local temperature changes). But if you have signifiant displacements (for a material with a very low stiffness for example), then the thermal drift shall be not be a problem.

It is usually better to measure this thermal drift before the test by maintaining a very low load for a given amount of time. A drift rate can then be evaluated and used to extrapolate the thermal drift during the test. The drift free displacement can then be evaluated after the test is over (your software may perform that correction automatically).

The thermal drift shall be evaluated prior to any measurement as it may vary from one test to another. It is very dependent on your device, your testing conditions, your material, etc. And last be not least, the shorter the test, the better it is (regarding thermal drifts issues).

In your case, if you do not have any trend, then you should not be influenced by thermal drifts. You may check that by performing your drift evaluation prior to your test.

Dear Yeraldinne Carrasco

Occasionally drifting occurring while performing the analysis it is inevitable to avoid for that you can have a reference given below this might clear your query

Real-time drift measurement for colloidal probe atomic force microscope: a visual sensing approach

I have estimated pHzpc of birnessite (found 2.4), pyrolusite (7.1), hausmannite (6.4), and manganite ( 8.1) using zeta potential measurements and got reasonable results. You can follow this method.

Hello

use Krylov-Newton Algorithm as algorithm and Total Relative Norm Displacement Increment Test for test command

Check http://resources.renishaw.com/en/download/brochure-xl-80-laser-measurement-system--81921

The rapid divergence of high resolution models from the actual state of the system is very well known and can be explained by the rapid error growth observed mainly for a fine scale part of the spectrum. The relative behavior of the error for the coarse and fine scales was described by Lorenz in his well known presentation on predictability.

The summary using Lorenz's original wording is as follows:

“1.Small errors in the coarser structure of the weather pattern – those features which are readily resolved by conventional observing networks – tend to double in about three days”

“2. Small errors in the finer structure – e.g., the positions of individual clouds – tend to grow much more rapidly, doubling in hours or less”.

Matthew Mabey, the output I obtained was based on 10 number of iterations. The perfect matching at the beginning is because I have used a time step of 0.05 for the natural period of structure. Because my solution of response spectra became erroneous at lesser period. However I am again trying to draw response spectra following the Newmark average acceleration method which is stable for any time step. In mean time I just wanted to know how do we correct the drift in velocity and displacement which is imminent due to the spectral matching procedure ???

Thanks.

Hey Sneha,

Yes, you can get the stress on the selected body. But, to select that specific part, first you have to split the part during modeling. For example: If you are using Solidworks, there is an option to split the structure into different parts.

https://www.mdpi.com/2076-3417/11/1/361

look at the manual for particle trackimg

@Defne - Do u think that your data has trend or drift? First, ensure it.

If you think that it has drift, only then go ahead with this result otherwise test stationarity without drift. If you find the series I(1) then go ahead with cointegration.

We had similar problem years ago, ended up being a vacuum leak. check ultimate vacuum, our rough vacuum was leaking slightly difficult to detect. and the instrument could not compensate. new rough pump and fittings fixed the problem.

dear check your definition

Not sure if this would help or perhaps you know this already:

Dear Nirmala

Generally, the capacity curve in the first mode of the base isolated structure is bilinear that consists of a large elastic part and a small plastic part, because the first mode represents the isolation system.

@Sharma, I agree with Dr. Sharma that time step influence the convergence in problems involving time such as seismic forces, explosive loading etc.

dear Andri , did u find the issue

Gerhard Martens Yes, you are right! The diffraction peak of pyrrhotite (Fe1-xS) can be found in the XRD pattern at temperature above 550°C. Interestingly, the baseline begins to shift from down to top as angle increases at the same temperatuer.

The heating rate is 10°C/min at the temperature range of 0~500°C and 700~900°C, while it is reduced to 5°C/min for the stage of 500~700°C. The soaking time is 2 hours for each temperature point.

I think the answer lies on the boiling point of TOTM. In my opinion, incomplete volatilization of the compound is happening, and your sample matrix acts as a better solvent than pure methylene chloride. I've seen this before with some pesticides and I had to modify the injection mode (from splitless to PTV) and/or using analyte protectants or doing matrix-matched calibration. Here are some of my suggestions:

-Try a split method with a ratio of 10:1; as you know, splitless injections tend to get the inlet dirty and your analyte is very likely behaving as a heavy matrix component. This will also enable you to set a higher inlet temperature in order to enhance analyte volatilization. If you think your samples are dirty and you can get enough sensitivity, I'd change to this injection mode.

-Test some other solvent like DMSO, to see if this solvent can clean the inlet more effectively. If you use DMSO as your injection solvent, be sure to use an adequate solvent delay time, since that solvent can give a very broad peak. Also, make sure your early eluting analytes aren't lost. Be careful of its expansion volume, make sure your liner volume can hold the sample gas volume; because of this, again, a split injection can be helpful, PTV can work too.

-Setting a higher ion source temperature would help you maintaining the ion source clean, but this parameter surely isn't the cause of your increased response. 150 C is indeed a very low temperature, I wouldn't work with such value unless I was interested in some specific ions, but then the samples should be very clean. It is a good idea though, to increase the ion source temperature, due to the high boiling point of your analyte and if you think your samples are dirty. You can also increase the transfer line temperature for a more stable response due to improvements in peak shape.

Dear Syed,

I think the drift velocity defines the response of the mobile charge carriers to the electric field provided that they are free to move and wander in the material because of the thermal energy. It is said the particles are free. This happens if the material is crystalline.

If the material is amorphous the charge carriers are lying in shallow potential wells meaning they are not free to move in long ranges. At first one applies the field till they come out of the wells and become free. Therefore appreciable mobility is measured first after crossing the mobility gap.

After the mobility gap the amorphous material behave like the crystalline materials. Under this case bone can speak from saturation velocity.

As for the micrcrystalline materials they have their conduction mechanism which is drift in the bulk of the grains and themionic emission or tunneling across grain boundaries.

In summary you can not speak for a saturation velocity for all materials.

Best wishes

Guanghui Zhu Thank you for the suggestion. Could you please elaborate it? I'm not sure how I can infer the separatrix position from radiation distribution or temperature and density profiles. In both cases, I think that I'd have to make some additional assumption, such as "upstream separatrix temperature is 70 eV" supported by the two-point model.

Hello Dear Professor,

A file address attached below is a study about torsion in concrete building in Chalmers University.

Best Wishes

Have you examined the particles of the volcanic deposits using polarised light microscopy to get an idea about mineral type (or types) are present? I would imagine that the assemblage of minerals in the deposits from a known volcanic source are well-documented (e.g., acidic with lots of silica - granitic, or basic with less silica - basaltic). The presence of coloured (possibly pleochroic) minerals would indicate pyroxenes or amphiboles or if the minerals are colourless then that would suggest silicates such as quartz, feldspar, etc. When using analytical techniques, such as vibrational spectroscopy, don't forget that a quick look down a microscope at your sample can give you lots of valuable information about its composition and if it is a mixture, how many phases, etc.

Dear Prof. Florencia Calaza

Thank you so much for the information.

It may be at any point. It is required to be check.

Gold nanoparticles around 100 nm diameter work ok; a little brighter than mEos2/3.2 with 561 nm excitation and typical RFP-ish emission filters. In my experience, something a bit brighter than that would be even better, but they work well enough if you have a few particles to average and/or have a moving average position over a few frames. Also because they aren't super bright they need to be in good focus. I would like to try >=200 nm diameter next time.

If you happen to be working with a fixed sample, an alternative is not using anything and using the data itself as drift correction; works if there is only slow xy drift.

That's odd. Usually the drift caused by charging is noticeable in all scanning speeds, although you see some distortion at lower speeds (photo quality) rather than movement. Furthermore, it is more evident in higher magnifications, since you are increasing the current density (smaller area and same current).

Did you try to use a conductive glue/paint or a carbon tape to fix your samples on the stub? I believe the hot glue is the cause of the drifting. You can also try to ground you sample with conductive paint/glue. If it is in a cover slip or other solid substrate, you can paint the edges. If it is a biological piece, you can try to paint the parts that are in contact with the hot glue and the hot glue itself. That could improve your grounding quality.

Good luck!

Why do you need to keep your specimen in SEM for days?

What is the purpose of multiple observations of the same are?

If you see your specimen moving, it could be because:

Charging. Check if your specimen is well grounded. Sometimes after sputtering there are not enough gold on vertical edges of cover plates.

Soft mounting (like with carbon tape).

Stage instability, especially if you use specimen tilt. Avoid tilting.

SEM instability. Run SEM with a beam on for some time before you start taking pictures.

R combines both the strength and deflection through the energy/toughness. Cd represents the deflection only. I personally attributes using smaller values for Cd than R to stability issues. Allowing Cd values close to R may be dangerous since the overall stability of the structure and surrounding elements of the seismic lateral system may be jeopardize and perhaps to control the second order internal forces.

John Hodge

See my CNPS page at:

Printed book is most complete. Free book almost the same, but without math.

Abstracts: For instance "Einstein was right - who was wrong.

From _______________ John-Erik

Why having the minimum base shear in the first place? - Because your building may have a very low actual seismic demand, but still needs to have some minimum lateral strength.

Why limiting the period for base shear calculation? - Because longer periods (after the spectral plateau) mean smaller lateral seismic forces. Introducing upper limit of the period you aim again at providing some minimum and reasonable lateral strength of the building.

Why not limiting the period for drift calculation? - Because when using the ASCE7 design spectra longer periods mean greater displacements/drifts. You aim at calculating correctly the displacements without artificially limiting them.

Combining all these requirements you'll have a conservative design for both strength (with the code minimum lateral force capacity) and stiffness (with the code maximum lateral displacements and drifts).