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This joint effort between educators and a microscope manufacturer is the first attempt at the development of a truly education‐oriented SEM suitable for use in the classroom.
Since the specimens were not likely to be frozen during quick examination and photography, this method might well be called “low-temperature SEM” (LT-SEM) as distinguished from “cryo-SEM”.
The method is easily adaptable to many types of tissue and suitable for any laboratory with standard SEM preparation equipment and a variable-pressure-SEM or tabletop SEM.
The present accomplishments of the SEM technique are not restricted by the resolution power of the microscope but rather by the unsolved problems concerning the preparation of specimens.
It is demonstrated that the resulting simulation package is capable of quantitatively predicting experimental SEM images and is an important tool in building a deeper understanding of SEM imaging.
The information obtained is useful for designing the SEM detector for specific applications.

Related Questions

How to present sem in research?4 answersStructural equation modeling (SEM) can be presented in research by following several steps. First, researchers should provide an overview of SEM and its applications in the specific field of study. They should explain the benefits and advantages of using SEM, such as its ability to analyze complex patterns of relationships among variables and validate theoretical models. Researchers should also describe the different types of SEM techniques, such as covariance-based SEM and variance-based SEM, and when each should be used. Additionally, it is important to discuss best practices for conducting SEM analysis, including the steps involved in building research models, testing measurement errors, and conducting confirmatory factor analysis. Finally, researchers should provide examples of SEM applications in the specific field of study to demonstrate the scope and diversity of SEM techniques.
What are the advantages of SEM?4 answersThe advantages of scanning electron microscopy (SEM) include its ability to provide imaging at various magnifications, ranging from 1 µm to 1 nm, depending on the hardware used. SEM allows for the scanning of surfaces and the collection of information on topographical contrast, material contrast, and compositional information through the emission of different signals. The simultaneous acquisition of secondary electrons and x-rays in SEM results in images with different spatial resolutions, which can be improved by reducing the accelerating voltage. SEM is a powerful imaging instrument that integrates with an energy-dispersive spectrometer for elemental analysis at the specimen surface. It is applicable to both experimental and non-experimental research, providing a confirmatory factor analysis approach to examine relationships among latent variables indicated by multiple observable variables. SEM is a robust and comprehensive analytical technique that can strengthen and increase the accuracy of analyses in medical and health research. Additionally, SEM is widely used in various research areas and industry sectors to study nanoscale features of samples and investigate biological samples.
What are the benefits of using SEM to model behavior?4 answersUsing structural equation modeling (SEM) to model behavior offers several benefits. Firstly, SEM allows for an in-depth investigation and analysis of behavior by examining the relationships between latent variables and observed variables. This enables researchers to understand the underlying factors that influence behavior and identify key intervention points. Secondly, SEM can incorporate multiple variables and their complex interactions, providing a comprehensive understanding of behavior. This allows for a more accurate representation of real-world phenomena and enhances the precision and explanatory power of the model. Additionally, SEM can be used to simulate and predict behavior over time, facilitating the study of dynamic evolution processes. Overall, SEM is a valuable tool for studying behavior as it offers a systematic and rigorous approach to understanding the complex relationships and mechanisms underlying human actions.
What is SEM-PLS?5 answersSEM-PLS (Partial Least Squares Structural Equation Modeling) is a method used to test predictive relationships between constructs by examining the presence of relationships or influences between them. It is particularly useful when dealing with non-normally distributed data patterns and when there is a limited sample size. SEM-PLS combines the strengths of structural equation modeling (SEM) and partial least squares regression (PLS), making it suitable for analyzing complex causal relationships in data with a high degree of collinearity and limited sample size. It allows for the examination of direct and indirect effects among latent variables and can handle multiple dependent variables. SEM-PLS has been used in various studies, including those related to mediating effects in structural equation modeling, factors influencing perceived value in flight ticket booking applications, the impact of social capital on economic development, factors affecting students' career adaptability abilities, and factors influencing digital literacy skills.
What is the difference between cryo SEM and other SEM techniques?5 answersCryo SEM is a technique that allows the characterization of complex drug formulations, biological samples, and soft-matter samples in their native hydrated state. It combines the advantage of studying hydrated samples with high-resolution imaging capability. On the other hand, other SEM techniques, such as room-temperature SEM, do not allow the study of samples in their hydrated state and may not provide the same level of resolution as cryo SEM. Cryo SEM also addresses challenges in imaging non-stained cryogenic biological samples, such as low contrast, curtaining, and charging artifacts, by using a cryogenic plasma FIB/SEM. This technique has been used to produce high-quality SEM images of a range of different biological samples, including bacteria, human cells, and tissue, with resolutions typically achieving 20-50 nm. In summary, cryo SEM offers the advantage of studying hydrated samples with high resolution, making it a valuable tool for the characterization of complex drug formulations and biological samples.
Why use PLS SEM in research?3 answersPartial least squares structural equation modeling (PLS-SEM) is used in research for several reasons. Firstly, PLS-SEM is often used when dealing with complex models, limited sample sizes, and the need for prediction. Secondly, PLS-SEM is preferred in certain fields like business marketing due to its ability to estimate and substantiate theoretically established models with constructs. Additionally, PLS-SEM offers new opportunities for researchers and practitioners in the business marketing field, providing a best practice orientation and supporting future applications. Furthermore, PLS-SEM can be used to explore and clarify research gaps, such as the role of mediating variables in e-commerce success. Finally, PLS-SEM allows for the evaluation of key factors defining quality perception, as seen in the analysis of ultra-premium red wines.

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What is CT Dose Index (CTDI)?
5 answers
CT Dose Index (CTDI) is a crucial measure of radiation dose output in computed tomography (CT) imaging. It is utilized to assess the radiation dose received by patients during CT examinations. Various studies have explored CTDI in different contexts, such as evaluating dose indices for different imaging protocols, studying size-specific dose estimates (SSDE) for different body regions, assessing radiation doses during practical CT examinations, validating methods for evaluating SSDE in lower limbs, and investigating the relationship between CTDI and body size indices in adult CT scans. These studies highlight the importance of CTDI in managing and optimizing radiation doses in CT imaging, emphasizing the need for accurate dose estimation and patient safety.
How does the chemical composition of Nb2O5 affect the redox properties of a GCE modified electrode?
5 answers
The chemical composition of Nb2O5 significantly influences the redox properties of a GCE modified electrode. Different studies have explored the synthesis and modification of Nb2O5 to enhance its electrochemical performance. For instance, the synthesis of Nb2O5/rGO composites has shown improved specific capacitance and electron transfer compared to rGO alone. Additionally, in situ doping of alkali metals in Nb2O5 has demonstrated a twofold enhancement in photoelectrochemical water splitting efficiencies, indicating improved charge carrier density and surface charge transfer. Moreover, Nb2O5/graphene nanocomposites have exhibited superior electrochemical conductivity and cyclic stability, making them effective electrodes for supercapacitor applications. These findings collectively highlight the crucial role of Nb2O5 chemical composition in enhancing the redox properties of GCE modified electrodes.
Aquaporin stain? what about vessel, skin ?
9 answers
Aquaporins (AQPs) are integral membrane proteins that facilitate the transport of water and, in some cases, small solutes like glycerol across cell membranes. Their expression and role have been extensively studied in various tissues, including the skin and blood vessels, revealing their significance in physiological and pathological processes. In the context of skin, AQP1 and AQP3 have been identified as key players. AQP1 is strongly expressed in the endothelia of most microvascular tissues outside the brain, including small blood vessels in the skin, indicating its role in water transport in these areas. However, its expression in tumoral melanocytes in atypical nevi and melanomas is notably absent, suggesting a differential expression pattern in benign versus malignant conditions. AQP3, on the other hand, shows strong labeling in melanocytic nevi, atypical melanocytic nevi, and melanomas, as well as in basal cell carcinomas and squamous cell carcinomas, highlighting its ubiquitous presence across various skin tumor types and its potential involvement in tumor physiology. The expression of AQP1 and AQP3 also varies in response to skin injury, with both showing increased expression in human skin wounds, correlating with wound age. This suggests their involvement in the wound healing process, possibly through regulation of water and glycerol transport in keratinocytes and dermal vessels. Furthermore, AQP3's role extends beyond water transport to implications in cell proliferation, migration, and tumorigenesis, with its expression being downregulated in certain skin cancers and inflammatory proliferative disorders, indicating a potential role in tumor differentiation and skin pathology. In summary, AQPs, particularly AQP1 and AQP3, play crucial roles in skin physiology and pathology, including hydration, wound healing, and tumor biology. Their expression in skin and vessels underscores their importance in these tissues, offering potential targets for therapeutic intervention in skin disorders and injuries.
What are the current machine learning algorithms used for detecting fake satellite images?
4 answers
Machine learning algorithms play a crucial role in detecting fake satellite images. Current research presents various approaches for this task. CLPNM-AD utilizes contrastive learning with prototype-based negative mixing for correlation anomaly detection, outperforming baseline methods and enhancing reliability. Another study focuses on employing hashing techniques like SHA-256 for integrity and security in ship detection from satellite imagery, ensuring tamper-proof image transmission. Additionally, a tractable Bayesian neural network approximation using Monte Carlo dropout aids in capturing uncertainty in satellite telemetry time series for anomaly detection, surpassing existing methods in accuracy and reliability. Furthermore, AutoML techniques, particularly Neural Architecture Search, are applied to optimize neural network configurations for object recognition in satellite imagery, enhancing performance across diverse datasets and scenarios.
What is InfraRed sensor?
4 answers
An Infrared (IR) sensor is an electronic device that detects and measures infrared radiation emitted by objects. These sensors are widely used in various applications, including detecting motion, measuring heat, and remote control systems. IR sensors work by utilizing infrared light sensors to detect specific wavelengths in the IR spectrum. They consist of an IR emitter (usually an IR LED) and an IR detector (such as an IR photodiode) that respond to IR light of the same wavelength emitted by the IR LED. Recent advancements include the development of zero-standby power IR sensors that can detect and generate oscillating waveforms based on incident IR power levels, offering new functionalities for remote wake-up sensors. Additionally, miniaturized NIR spectral sensors have been designed for portable use, demonstrating comparable performance to traditional spectrometers in applications like moisture quantification and material classification.
Why Cu 100 is not detected in XRD?
4 answers
Cu{100} may not be detected in X-ray diffraction (XRD) due to various reasons outlined in the provided contexts. For instance, in the study by AlShamaileh et al., the focus was on Cu{100} surfaces with Pt or Pd deposition, not on pure Cu{100} surfaces. Shirokoff and Lewis discussed Cu-implanted SiO2 films and nano-crystalline Cu phases, which may not directly relate to pure Cu{100} surfaces. Additionally, Dooraghi et al. concentrated on CdTe detectors for X-ray computed tomography, which is unrelated to the detection of Cu{100} in XRD. Cappadonia et al. investigated Cu deposition on Au{100}, not Cu{100} itself. Vlieg et al. explored Cu(100)-(2×2)-S systems, which do not directly address the detection of pure Cu{100} in XRD. Therefore, the absence of direct references to pure Cu{100} surfaces in the provided contexts explains why Cu{100} detection in XRD is not discussed.
What are the practical applications of CB-SEM in various fields, such as psychology, sociology, and public health?
7 answers
Covariance-based Structural Equation Modeling (CB-SEM) serves as a robust multivariate statistical analysis technique, widely applied across various fields including psychology, sociology, and public health, due to its ability to evaluate complex relationships among latent constructs. In psychology, CB-SEM is utilized to understand latent phenomena such as consumer perceptions, attitudes, or intentions and their influence on organizational performance measures, highlighting its importance in examining the intricate relationships that shape human behavior and organizational outcomes. This application is further supported by the emphasis on best practice recommendations for testing models and reporting findings within the psychological research community, ensuring the method's misuse is minimized and its analytical potential is fully leveraged. In the field of sociology, CB-SEM's application is evident in its use to investigate moderation and latent interaction effects, providing a nuanced understanding of the social structures and relationships that govern societal dynamics. This capability is crucial for sociological research, where the interplay between various social factors and individual behaviors needs to be disentangled with precision. Public health research benefits from CB-SEM through its application in tourism and hospitality research, where understanding the relationships between different variables is crucial for improving service quality and customer satisfaction. Although not directly mentioned in the context of public health, the methodological rigor and analytical depth provided by CB-SEM in tourism and hospitality research underscore its potential applicability in public health studies, especially in areas related to health service quality and patient satisfaction. Furthermore, the widespread application of SEM, including CB-SEM, in business, psychology, and industry, underscores its versatility and effectiveness in addressing complex research questions that involve multiple variables and constructs, such as employee motivation and its influencing factors. This versatility makes CB-SEM a valuable tool not only in the aforementioned fields but also in a broad range of disciplines seeking to uncover the underlying mechanisms of complex phenomena.
What are the applications of variational inference?
5 answers
Variational inference finds applications in various fields such as machine learning, astrophysics, and dynamic network analysis. It is utilized for parameter estimation, model selection, and latent space modeling. In machine learning, variational inference transforms posterior reasoning into an optimization problem, deriving posterior distributions by solving this problem efficiently. In astrophysics, variational inference offers a faster alternative to Monte Carlo techniques like MCMC for parameter estimation and model selection, making it ideal for handling large datasets and parallel processing. Additionally, variational inference is applied to dynamic network data analysis, providing a quicker method compared to Markov chain Monte Carlo algorithms, especially beneficial for large networks. The versatility and efficiency of variational inference make it a valuable tool in diverse scientific domains.
What is the formula for calculating combined standard deviation?
5 answers
The formula for calculating combined standard deviation can vary based on the context and the specific requirements of the analysis. Various studies have highlighted different approaches to improve the accuracy and efficiency of combining standard deviations. For instance, the Cochrane Collaboration provides a formula for combining size, mean, and standard deviation from two groups, particularly useful in meta-analysis. Additionally, research has shown the unreliability of traditional PERT-type formulas and introduced alternative, more accurate formulas for standard deviation calculations. Moreover, the use of computer programs implementing algorithms like the Monte Carlo method has been proposed for calculating combined standard uncertainty with high accuracy. These diverse methods aim to enhance the precision and reliability of standard deviation calculations in various analytical scenarios.
What are the positives and challenges of policy evaluation in developing countries?
5 answers
Policy evaluation in developing countries presents both positives and challenges. Positives include the potential for quantitative assessment of impact on development investments, the aim to improve data efficiency while maintaining unbiasedness in Monte Carlo methods, and the focus on addressing societal problems through targeted interventions. Challenges involve the complexity of assessing program effectiveness and value for money, requiring adequate economic modeling tools and decision-making processes. Additionally, factors like non-linear information processing costs and political bias in program selection can distort evidence and policy choices during implementation at scale in less-developed countries. These challenges highlight the need for rigorous evaluation methods and evidence-based policy approaches in the context of developing countries.
Are there challenges in evaluation of different sector policies?
5 answers
The evaluation of different sector policies presents various challenges.highlights uncertainties in policy objectives, causality relationships, data acquisition difficulties, and official resistance as key obstacles in evaluation studies.emphasizes the complexity of evaluating entrepreneurship policies due to the systemic and evolutionary nature of entrepreneurship, which questions the assumptions of traditional quantitative evaluation methods. Additionally,stresses that measuring the average impacts of public interventions may not effectively inform policy-making, especially in innovation policy where the focus should be on changing the behavior of supported firms rather than just numerical outcomes. Overall, challenges in evaluating sector policies include the dynamic nature of policy issues, methodological limitations, and the need for a more evidence-based and nuanced approach to policy evaluation.