Experimental methods in chemical engineering: Scanning electron microscopy and<scp>X</scp>‐ray ultra‐microscopy—<scp>SEM</scp>and<scp>XuM</scp>
Bibliographic record
Abstract
Abstract Scanning electron microscopy (SEM) produces images at 500 000 times magnification and better than 1 nm resolution to characterize inorganic and organic solid morphology, surface topography, and crystallography. An electron beam interacts with the material and generates secondary electrons (SE) and backscattered electrons (BSE) that detectors capture. Coupled with X‐ray energy‐dispersive spectroscopy (X‐EDS), SEM‐EDS identifies elemental composition. X‐ray ultra‐microscopy (XuM) traverses particles to identify phase changes and areas of high density and voids without slicing through the solids by microtome. Although SEM instrument capability continuously evolves with higher magnification and better resolution, desktop SEMs are becoming standard in laboratories that require frequent imaging and lower magnification. Hand‐held cameras (800–1500×) have the advantage of low cost, ease of use, and better colours. SEM depth of field is better than visible light microscopy, but image stacking software has narrowed the gap between the two. Modern user interfaces mean that today's SEM instruments are easier to operate and data acquisition is faster, but operators must be able to select the right technique for the application (e.g., SE vs. BSE). Furthermore, they must understand how operating parameters like probe current, accelerating voltage, spot‐diameter, convergence angle, and working distance compromise sample integrity. The number of articles the Web of Science indexes that mention SEM has grown from 1000 in 1990 to over 40 000 in 2021. A bibliometric map identified four clusters of research: mechanical properties and microstructure; nanoparticles, composites, and graphene; antibacterial and green synthesis; and adsorption and wastewater.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.032 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".