Experimental methods in chemical engineering: Transmission electron microscopy—TEM
Bibliographic record
Abstract
Abstract Thanks to an accelerating voltage in the range of 30 to 300 kV, an electron beam can pass through a thin specimen and form an image with sub‐Ångström spatial resolution. When impinging on a thin crystalline specimen, the fast electrons scatter and diffract. The transmitted electron pattern depends on the local thickness, density, crystal structure, and chemical nature of the sample. The transmission electron microscope (TEM) shapes the incoming electron beam using magnetic lenses onto the specimen and, using a different set of magnetic lenses, focuses the projected electron pattern to a camera. The final image magnification and contrast are controlled using the parameters from the electron gun, apertures positioned along the optical path, and magnetic lenses. With this combination of lens and aperture, TEM offers two possible modes of operation: (a) imaging, including high‐resolution electron microscopy to reveal the size, shape, crystallinity, and morphology of materials; and (b) diffraction, to determine the crystalline nature of a region of interest of a thin film, particle, or collection of particles. Chemical engineers have taken advantage of both of these modes to analyze their samples and inform their research. A bibliometric study conducted using the WoS database places TEM as one of the preferred microscopy tools to study advanced materials such as thin films, nanomaterials, and composites used in particular for the development of applications related to energy storage and conversion (catalysis, photocatalysis, electrochemistry, and batteries) and environment (adsorption, waste‐water treatment, and filtration).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.026 |
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".