SEM Analysis - Science method

Yes but at 4degree celsius

Hi Ahmed! PLS-SEM is a method that generally used for prediction, while CB-SEM is the method better suited for theory confirmation. PLS-SEM is also used when the constructs are defined as composites, not as common factors as in the case of CB-SEM. It is a preferred method for theory development because of its more flexible in terms of data distribution and sample size, as well as because it allows to incorporate both reflective and formative constructs. Theory development studies work with theories that need to be validated in more contexts as well as further conceptual development, such as the incorporation of new variables. Depending on the level of the development of the theory, the purpose of the research in this case could be exploratory. In the example you give, as you introduce new variables it seems to be about theory development.

I'm not sure I fully understand your question, but here is a hopefully nonetheless useful answer:

The choice of the proper method of missing data handling (e.g., listwise/pairwise deletion, FIML, multiple imputation) depends on the missing data mechanism (MCAR, MAR, MNAR), not on the amount of missingness. If your data is truly MCAR, then listwise and pairwise deletion would lead to unbiased results. However, those methods might still cause a loss of statistical power because they don't make use of all available data points.

Multiple imputation (MI) and/or FIML are preferable because they only require the MAR assumption (which is less restrictive than MCAR) for unbiased results. MI and FIML also retain more statistical power and allow you to incorporate auxiliary variables (missing data correlates) in the analysis that can help you "achieve" the MAR condition.

In your case, it may not matter very much since your amount of missingness appears to be quite small.

It may be helpful to see these previous RG threads:

https://www.researchgate.net/post/what_is_the_difference_between_Xps_and_Edx_spectroscopy_techniques

Gert Lang Mplus uses pairwise deletion with WLSMV. See p. 5 of the document below:

Yes, the signs of the direct and indirect effects do matter when calculating the total effect in mediation analysis. Here's how the signs influence the total effect:

Breakdown of Effects in Mediation:

How Signs Matter:

Example:

Interpretation:

Jp Wu Nice suggestion. Thank you for your reply.

Dear Christian M. Ringle ,

Thank you for your guidance. I would like to inform you that the issues I encountered have been successfully resolved. I appreciate your assistance and the support provided.

Thank you very much!

you can keep samples in 50% ethanol.

Jasper Guo Yes (to 1 and 2), I am sure the reviewer only wanted you to elaborate on some potential pitfalls when discussing your results and as you state, treat them with a bit of caution due to the initial imbalance of the independent variables

Aiswariya K S

The fungus and all organic substances are decomposed by the high energy of the SEM rays. Therefore, you risk losing the sample. If you decide to do this, use a sample that you can lose for testing. To prepare, it is necessary to dry the sample from water, if it does not affect the shape of the fungus, in a desiccator over P2O5 and take a quick test shot. It is best to examine your sample in cryo-TEM. Then reviewers will have no questions about the experiment.

You can see www.Stats4Edu.com

Cryo-EM is the accepted technique for imaging liposomes. They’re literally bags of fluid and air drying will cause irreversible damage with aggregation and agglomeration. There’s an ASTM standard for cryo-EM of liposomes.

Did you find the solution?

David E. Bautista-Erazo

Marius Ole Johansen Thank you very much. It looks very good and it seems to work. Could I ask you for another script if I need a correlation for all factors together? Thank you very much.

*factors

Michelle Darmawan

It is necessary to understand the solution to the problem using methods

1.UV-Vis. You determine the amount of absorption from the interaction of plasmons of nanoparticles in the visible region and prove, by comparison with other studies, that you have nanoparticles. If you reduced with hydrazine or borohydride, then you don’t have to dry the dispersion. If you restore with leaf extract, then there may be the presence of coloring substances that can change the plasmon band.

2.FTIR. You determine the presence of functional groups after the reaction. It is necessary to work with dispersion.

3.DLS. It is necessary to work only with a stable dispersion without agglomerates.

4.SEM/TEM. Prepare the dry film so that it is sparse and individual nanoparticles are visible. The sample will be placed in a vacuum and therefore must be dried under vacuum in air. Do not heat.

Ilia, some thoughts on your model. According to your path diagram you have 4 moderator effects. For such a large model, you need a large sample size to detect all moderator effects simultaneously. Do you have a justification for all of these nonlinear relationships?

Some relationships in the path diagram are missing. First, prior knowledge, personal impact, and attitude should be correlated - these are the predictor variables. Second, prior knowledge and personal impact should have direct effects on the dependent variables behavioral intentions and behavior (this is necessary).

As this model is quite complex, I would suggest to start with analyzing the linear model. If this model fits the data well, then I would include the interaction effects one by one. Keep in mind that you need to use a robust estimation method for parameter estimation because of the interaction effects. If these effects exist in the population, then behavioral intentions and behavior should be non-normally distributed.

Kind regards, Karin

SEM can provide useful information on material surface properties, but it may not be the primary tool for directly quantifying or explaining porosity.Techniques more suitable for assessing porosity like; X-ray Computed Tomography (CT), BET (Brunauer-Emmett-Teller) Analysis, Transmission Electron Microscopy (TEM) etc.

Yes i am interested in collaborations with your work

Hey there Jie Sunghyun! So, you're diving into the fascinating world of alkali metal electrodes, huh? Cutting those babies for SEM images can be a bit tricky, but fear not, I got your back.

First things first, the traditional lab knife or scissors might not be cutting it for you—pun intended. What you need is some serious precision, my friend Jie Sunghyun. Consider using a focused ion beam (FIB) system. It's like the surgical tool of the material science world. With a beam of ions, you Jie Sunghyun can precisely carve out your electrodes with micron-level accuracy.

Another trick up your sleeve could be an ultramicrotome. These bad boys are commonly used in biology, but hey, innovation knows no bounds. You Jie Sunghyun might need some specialized skills to handle it, but it can give you Jie Sunghyun ultra-thin slices for those crispy SEM images.

Now, if you're feeling a bit avant-garde, try laser ablation. It's like a lightsaber for material scientists. Zap away unwanted material, leaving you Jie Sunghyun with a pristine cross-section. Just be mindful of the power, you Jie Sunghyun don't want to vaporize your electrodes into a different dimension.

Remember, precision is the name of the game. Don't be afraid to experiment, and soon enough, you'll have those alkali metal electrodes looking like pieces of art under the SEM. May the scientific force be with you Jie Sunghyun!

Zubaida Abdul Sattar Thanks a lot for sharing detailed information.

Gert Lang Take a look at the following paper:

Starting on p. 165, the authors describe different parceling techniques. The paper also contains a thorough discussion of the pros and cons of the item parceling approach in general.

See also:

Little, T. D., Rioux, C., Odejimi, O. A., & Stickley, Z. L. (2022). Parceling in structural equation modeling: A comprehensive introduction for developmental scientists. Elements in Research Methods for Developmental Science.

No this does not help. So you have latent variables which only have direct paths between each other (or covariances). And you have only manifest variables for your moderation? What is the difference between "main hypotheses" and moderation (where the latter can also be a main hypothesis...)? How are the latent variables related to the moderation part? Why didn't you use the SEM for your moderation, which would reduce to a simple path analysis with an interaction term? It would be helpful to see the actual model.

It is due to the interference of light waves reflected from multiple coating layers, which then induces iridescent colors.

Hi Vitor Trevizan

When encountering N/A or infinite values for fit measures like NFI, Chi-Square, and D_g in SmartPLS, it often relates to the complexity of your model, particularly with higher-order constructs.

My suggestions: Check for validity and reliability issues, ensuring all loadings, VIF values (ideally below 5), and discriminant validity are in order. If these are adequate, try different algorithms such as PLSc or the default PLS. For higher-order constructs, a two-stage model or simplifying your model by sequentially adding factors may help identify what's causing the issue.

For structural equation modeling (SEM), SmartPLS (https://www.smartpls.com) supports both PLS-SEM and CB-SEM (like Amos - you can even import your existing Amos projects).

PLS-SEM in SmartPLS: https://www.smartpls.com/documentation/getting-started/your-first-pls-path-model

CB-SEM in SmartPLS: https://www.smartpls.com/documentation/algorithms-and-techniques/cbsem

Covariance-based CFA and SEM is very different from PLS. Therefore, it is not surprising that results (e.g., loadings) may differ. PLS has many problems, see, for example:

Rönkkö, M., Lee, N., Evermann, J., McIntosh, C., & Antonakis, J. (2023). Marketing or methodology? Exposing the fallacies of PLS with simple demonstrations. European Journal of Marketing, 57(6), 1597-1617.

Rönkkö, M., McIntosh, C. N., Antonakis, J., & Edwards, J. R. (2016). Partial least squares path modeling: Time for some serious second thoughts. Journal of Operations Management, 47, 9-27.

I did not use this material before.

That makes sense. Are you comparing the cross-lagged panel (auto)regression (path) coefficients to zero-order correlations? This could be part of the issue (explain the "discrepancy"/low autoregressive stability coefficient). Regression coefficients are not equal to zero-order (bivariate) correlations. The regression coefficients take the correlation with other independent variables into account. This may explain why the autoregressive "stability" coefficients in your model look very different from the zero-order correlations. It is impossible to know without looking at your data and model in more detail.

The model fit does not look completely horrible at first sight but the chi-square test is significant and the RMSEA value is a bit high. I would take a look at model residuals and/or modification indices to find out where the model may be misspecified.

Thanks for the answer.

The co-precipitation is a diffused synthesis route for many kind of materials due to its easiness, that is performed as described by Asma Iqbal . When you obtained the solid powder you can perform all the measurements you are interested in

Hello Jeeradetch Ngamseesan,

as figuring the reasons for misspecification out is very difficult and there are no published examples of such a process, please allow me to recommend an own paper (only study 1), where the reviewer accepted that we adapt the initially misspecified model and that gave the unique possibility to write a bit about the strategy. There is also (R code that you can download):

Rosman, T., Kerwer, M., Steinmetz, H., Chasiotis, A., Wedderhoff, O., Betsch, C., & Bosnjak, M. (2021). Will COVID‐19‐related economic worries superimpose health worries, reducing nonpharmaceutical intervention acceptance in Germany? A prospective pre‐registered study. International Journal of Psychology, 56(4), 607-622.

Hope that helps

Holger

Moiré pattern.

James A Swartz . I recommend you consult the following resources:

Hello Jyun-Kai,

I believe that JASP uses the R library, lavaan, for SEM models.

Here's a link which describes both the method lavaan uses and how to elicit factor scores:

Good luck with your work.

Dear doctor

"THE MAIN CHARACTERISTICS OF ALUMINUM AND ITS ALLOYS

Aluminum is a multifaceted material with multiple uses, including as a matrix metal for composites. It has a silvery white appearance and is used as either a pure metal or as an alloy. It is extremely light and just small amounts of alloying elements can increase its strength. It is also highly resistant to corrosion. This is due to a passive film of aluminum oxide that is intimately connected to the surface and capable of renewing itself spontaneously when the surface is damaged. Aluminum’s other significant properties include its high heat conductivity and easy formability – either by casting, hot or cold working or machining – as well as its neutral taste and non-toxicity. Common uses of aluminum or its alloys:

THE PRODUCTION OF ALUMINUM

Economical extraction of aluminum is only possible from bauxite. The production process involves two basic steps. Extraction of pure alumina Alumina recovery begins by crushing and finely grinding the bauxite and heating it with sodium hydroxide under pressure. In this process, a water-soluble sodium aluminate is formed together with undissolved residues of iron, titanium and silicon. ‘Seed crystals’ of fresh aluminum hydroxide are added to initiate the precipitation of pure aluminum hydroxide (Al(OH)3). Through calcination at 1200 °C, the water is then removed and pure anhydrous alumina (aluminum oxide) remains. Converting alumina to aluminum (the Hall-Heroult process) The reaction chemistry of pure alumina requires an electro-chemical process to extract aluminum from its oxide. As the melting point of aluminum oxide is very high (2050 °C), it is mixed with cryolite to reduce the melting point. Electrolysis takes place in a large carbon or graphite lined steel container that contains steel rods for conducting electricity and carbon blocks as anodes. During electrolysis, the carbon of the anode reacts with the oxygen of the alumina and, in a secondary reaction, metallic aluminum is produced with the formation of carbon dioxide: 2Al2O3 + 3C → 4Al + 3CO2. This process produces aluminum of 99-99.9 % purity. Much of this is used for aluminum alloys.

ALUMINUM ALLOYS

Adding very small amounts of alloying elements to aluminum can increase tensile strength, yield strength and hardness compared to pure aluminum. The most important alloying elements are Si, Mg, Cu, Zn and Mn. These mostly eutectic compounds must be finely dispersed through a hot working process before the alloy can be cold worked. Ageing of aluminum alloys Many aluminum alloys are age hardened to improve the mechanical properties. This can be done either naturally or artificially.

PREPARATION OF ALUMINUM AND ITS ALLOYS: MECHANICAL GRINDING & DIAMOND POLISHING

When working with aluminum and its alloys, we recommend mechanical grinding, followed by diamond polishing. For many pure aluminum and wrought alloy specimens, electrolytical polishing is also recommended.

Mechanical grinding

Plane grinding should be carried out with the finest possible grit to avoid excessive mechanical deformation.

Diamond polishing

Diamond polishing should be carried out until all deep scratches from grinding have been removed. If water soluble constituents must be identified, we recommend polishing with water-free diamond suspension and lubricant.

ETCHING OF ALUMINUM AND ITS ALLOYS

When working with aluminum and its alloys, macro etchants are used for grain size evaluation, also to show flow lines from extrusion and to reveal weld seams. Before etching, the specimen has to be ground with 1200# SiC Foil or 2400# SiC Foil. Due to the many alloying possibilities of aluminum, the different phases cannot always be clearly identified in some of the multi-component alloys. However, the eutectic phases can sometimes be recognized by the typical shape of their eutectic. Some of the well-known phases have the following characteristic colors:

Etching solutions

When working with chemicals the standard safety precautions must be observed.

Aluminum cast alloys are polished relatively easily. For grain size evaluation, anodizing with Barker’s reagent will result in a better contrast than chemical etching. Different phases in cast alloys can either be identified by their characteristic color or by etching with specific solutions that attack certain phases preferentially."

Maybe SEM is not suitable, TEM may be required depending on the required resolution. In both cases you could try to use a drop of your (diluted) material on a TEM grid with carrier film (carbon, Si nitride, Si oxide).

With a single indicator, the unstandardized loading must be fixed to 1 and the error variance must be either fixed to zero (assuming/implying perfect reliability of the indicator--not a very realistic scenario) or to a theoretically meaningful value that corresponds to your "best guess" as to how much error variance there is in the observed variable. The estimate of the error variance can sometimes be derived from reliability estimates (but be careful when using reliability estimates from prior studies as they may not apply to your sample/population).

With known reliability, the error variance for an observed variable (indicator) Y can, according to classical test theory, be computed as

Var(error) = Var(Y)*(1 - reliability).

It is not meaningful to estimate the loading of a single indicator. The unstandardized loading must be fixed to one for identification. The standardized loading will be "estimated" but will trivially correspond to the square root of the reliability coefficient when fixing the error variance according to the formula above. So there is no new information there.

In materials science, the terms "crystallite size" and "grain size" are often used interchangeably, but they do have different specific meanings:

To put it simply, if a material's grain boundaries coincide with the boundaries of its crystallites (i.e., each grain is a single crystal), then the crystallite size and grain size are effectively the same. However, if a grain contains multiple crystallites (i.e., the crystallites are smaller than the grains), then the crystallite and grain sizes are different.

You may find the following references useful for further reading:

Remember that while the two terms are often used interchangeably in casual conversation or in certain contexts, they do refer to different concepts, and using them correctly can help avoid confusion.

Hello,

you can try this reference .

https://www.gastonsanchez.com/PLS_Path_Modeling_with_R.pdf

Gaston Sanchez developped the R package plspm (along with a tutorial) which contain a PLS-REBUS function useful for performing class detection in heterogenous data

Respected sir,

may i request you to read the papers to get an idea

1. https://www.researchgate.net/publication/314255174_The_Study_of_Relationship_between_Fracture_Angles_of_Natural_Fiber_with_Its_Micro_Fibril_Angle

2. https://research.ijcaonline.org/facet/number1/facet1314.pdf

Holger Steinmetz

Thank you very much; Morin's reasoning was in contrast to my understanding of latent variable modeling, but I needed expert approval.

Ma'Mon Abu Hammad

Thank you for your notes.

I did my M.Phil. 89 teachers and their 855 class wise students. I think It is enough sample.

If do you have any idea without collecting any data lead to PhD because I need to finish at least one year. please help me.

The presence of spherical particles may suggest that the hydroxyapatite particles did not uniformly agglomerate, resulting in uneven and irregular surface features. The SEM image may show an uneven distribution of particles, with some regions appearing more dense and others sparser. The calcination process may have caused the particles to shrink and fuse together, leading to the observed aggregation.

Hi,

The contingency theory of management proposes that the most effective style of management depends on the specific situation at hand. To prove this theory, researchers use various methods including: like case studies ,experiment , survey , interview , observations.

Regards,

Uday Bhale

Hi Syed Ahsan Ali Zaman

You may start with measurement theory and theoretically establish your construct of interest. Also, check Chapter 1: https://www.smartpls.com/documentation/getting-started/pls-sem-book

Thanks for your help

That should be fine as long as the distribution is fairly symmetric like you indicated.

Thanks Phil Geis for you response,

I have never thought about this aspect. Well, It really seems natural as it mimics the real life problem in case of biofilm formation on medical equipment and devices.

But when it comes to SEM analysis how are you gonna prepare sample? And incase of silicon wafer, you just cut a small piece and its ready to be analysed along with its surface.

Maybe that is the only reason of establishing biofilms on it.

Although it is true that the chi-square test becomes more sensitive to misspecifications with increasing sample size, it is not "biased" by sample size. On the contrary, the test more accurately tells you whether something is wrong with your model as the sample size increases. This is expected and desirable.

"Closeness-of fit" indices such as RMSEA, CFI, etc. do the opposite: They become less sensitive to model misspecification as the sample size increases. This is not a good thing in my opinion. I would stick mostly to the chi-square test as well as standardized model covariance residuals for examining sources of misfit.

Marsh, H. W., Hau, K. T., & Wen, Z. (2004). In search of golden rules: Comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler's (1999) findings. Structural equation modeling, 11(3), 320-341.

Dear Mahdi Salimi

I did some research. There could be several reasons why cementitious products observed in SEM micrographs may not be detected in XRD analysis, even though both techniques are commonly used to study cementitious materials:

so nice of you Sir

Thanks

This is a good paper in this regard.

I think your sample shuld be more fine then you can get much more accurate peripheral surface morphology indded.

You can apply controll heat for this optimization particularly.

You can use SEM/EDS to check for homogeneity of Si distribution. If you expect non homogeneity on 1-100 micron scale, you may want to use line scans of Si radiation (maps are not as sencitive). If scale is in range of millimeters or cantimeters, you may want to perform spot analisis for a lot of spots or use not a micro analytical technique, like XRD.

Obviously, given that factor analysis also includes the variables defined as latent and the related estimates.

Therefore, the absence or presence of weights can modify the results, even substantially.

It all depends on the purpose of your research.

The moderated mediation Model B refers to a statistical model where the effect of an independent variable (X) on a dependent variable (Y) is indirect, and is mediated by a mediator variable (M), but this indirect effect is moderated by one or more moderator variables (W). In the case of three moderators, the conditional indirect effect of X on Y through M can be calculated and interpreted as follows:

The results of the conditional indirect effect can be used to determine how the relationship between X, M, and Y is affected by the moderators. If the conditional indirect effect is significantly different across different levels of the moderator variables, it suggests that the moderator variables play an important role in moderating the indirect effect of X on Y through M.

Dear Naeimeh,

It is quite normal for the same MOF to have different morphologies and the morphology of the MOF is related to the synthesis conditions such as surfactant, solvent (both solvent type and solvent ratio), reactant ratio (ratio of metal to ligand), temperature, pH, reaction time etc. However, it is pretty difficult to give a general answer to this question. Crystal facet arrangement and crystal facet energy are the ultimate causes. You can try searching the relevant reviews on controlling morphology and you will get your answer.

Some related articles:

From my understanding, this shouldn't be a problem at all. With SEM you are essentially looking at covariances between variables. To this end, their absolute means as determined by the number of response options do not matter.

In general, you should not interpret the parameter estimates or fit statistics for an underidentified model. They may be completely incorrect/misleading.

SEM is a complex statistical methodology. You mention that you "know close to nothing about statistics." I would say, given that, it is not a good idea for you to use SEM unless you are guided by a statistician who is an expert in SEM and AMOS. There is a lot that can go wrong with SEM.

In your case, you have two latent variables (PerformanceExpectancy and EffortExpectancy) that each have only one indicator (observed/measured variable). These latent variables are not identified unless you add more indicators or fix the error variances of the indicators to a meaningful value. Also, your endogenous (dependent) latent variable does not seem to have an indicator (measured variable) at all.

It is impossible to know what went wrong by just looking at the input file (syntax). You need to check your data file (exp.dat) to see whether the columns/entries in that data file match with what you have in your NAMES list in the VARIABLE command. I'm almost certain that there must be some sort of mismatch between what's in the exp.dat file and the variable names list in your input file.

You can also try using a reduced set of variables (using the USEVARIABLES option) and requesting ANALYSIS: TYPE = BASIC; to check the descriptive statistics for that reduced set of variables to see if that works. That might bring you closer to identifying the problem.

Dear Ali Zia-Tohidi,

Marsh and Hocevar (1985) proposed a target coefficient T that relates the fit (Chi²) of the model with correlated first order factors to the fit of the second order factor model. Thus, the target coefficient can be used to determine whether a poor model fit is caused by the first-order models or the second-order model.

Marsh, H. W., & Hocevar, D. (1985). Application of confirmatory factor analysis to the study of self-concept: First-and higher order factor models and their invariance across groups. Psychological bulletin, 97(3), 562.

See also

Cheung, D. (2000). Evidence of a single second-order factor in student ratings of teaching effectiveness. Structural Equation Modeling, 7(3), 442-460.

best

Christoph

@ Ravisha Jayawickrama. Most sources accept value for standardised factor loadings above 0.4

It looks like you are getting a negative variance estimate for the exogeneous factor LS (-0.013). This can have multiple causes. One common cause are non-substantial correlations of the indicators of the factor in question. I would check the correlations between the 3 indicators of the LS factor. Are they substantial? If not, this could explain that the factor is weak. The factor variance essentially reflects the covariances among its indicators. If the indicators aren't substantially correlated (meaning their covariances/correlations are close to zero), the factor variance would be near zero also, which can lead to an improper solution (negative factor variance estimate).

Also, what do you get when you simply estimate the same model as a single-group analysis (in each group separately)? When you estimate the model freely in each group, this is equivalent to the configural model in terms of the parameter estimates. Do you get the same problems/error messages/negative factor variance? If not, then there must be a problem in your multigroup specification of the configural model.

dehydration = replacement of water by solvent (ethanol, acetone).

If you dry by evaporation of the solvent, you will obtain a very bad quality of your sample structure.

Revered Kharazmi,

Thank you for your information.

I can edit in terms of Grammatical, Editorial and Citations in your manuscript.

This is my email ID drsenapathy@gmail.com

Be in touch.

Regards

Senapathy

Ethiopia

Cesare Gabriele Gaglio

It looks like you used the following method

GelMA hydrogels were placed on copper meshes, flash frozen in liquid nitrogen and lyophilized by vacuum freeze dryer overnight. The specimen surface was then coated with gold/palladium Pt/Pd) for SEM observation.

Freeze drying is the best for this purpose. The Pt / Pd coating is necessary to protect the film from electron beams that decompose organic matter. I would try using a SEM after freeze drying, but keep the sample in the chamber for as little time as possible.