Experimental methods in chemical engineering: Fourier transform infrared spectroscopy—FTIR
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.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.029 |
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".