Experimental methods in chemical engineering: Ultraviolet visible spectroscopy—UV‐Vis
Bibliographic record
Abstract
Abstract UV‐vis spectroscopy is an inexpensive, simple, flexible, non‐destructive, analytical method appropriate for a wide class of organic compounds and some inorganic species. UV‐vis spectrophotometers measure the absorbance or transmittance of light passing through a medium as a function of the wavelength. Chemical engineers apply it for quantitative analysis, to derive liquid phase reaction kinetics, and to identify the mechanism at the molecular scale. High performance liquid chromatography and ultra‐high performance liquid chromatography integrate UV‐vis detectors to identify and quantify the concentration of compounds in liquid streams. Combining these techniques with mass spectrometry facilitates identifying all species. UV‐vis diffuse reflectance spectroscopy is a variant with enhanced scattering properties that measures the properties of solids and powders. A bibliometric analysis of the 10 000 most cited papers referring to UV‐vis (2016 and 2017) groups research in four major clusters: nanoparticles and nanostucutres; photocatalysis and water treatment; crystals, complexes, and derivatives; and Ag and Au nanoparticles biological interaction.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 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".