Experimental methods in chemical engineering: Fourier transform infrared spectroscopy—FTIR
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract When molecules absorb infrared radiation (IR), their vibrational mode—stretching and bending of the electric dipole—changes to an excited state. Functional groups in organic molecules absorb IR related to their characteristic vibrational modes. A Fourier transform infrared absorption (FTIR) analyzer measures the absorbed IR to identify molecular composition of surfaces, structural and geometric isomers, orientation in polymers and solutions, and quantify impurities. We describe the power of FTIR instruments and their basic operating principles, including the main experimental setups available: transmission, diffuse reflectance (DRIFTS), reflection adsorption infrared spectroscopy (RAIRS), and attenuated total reflection (ATR), including the recent advances related to time‐resolved and operando applications. In catalytic studies, FTIR spectroscopy has demonstrated its versatility over the last several decades to understand reaction mechanisms, measure gas phase composition, and identify active sites. Over 3000 articles include catalysis and FTIR as keywords but 50 000 articles per year mention IR. We generated a bibliometric map of keywords in articles that Web of Science indexed in 2016 and 2017. The map identified four broad clusters of research related to or applying FTIR: nano‐composites, composites, and mechanical properties; nano‐particles, degradation, graphene oxide, and photo‐catalysis; adsorption, aqueous solutions, and waste water; and drug delivery, silver and gold nano‐particles, green synthesis, and antibacterial activity. Together with a synopsis of the principals of IR spectroscopy and a review of the applications, we discuss uncertainties and limitations of the technique.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it