Experimental methods in chemical engineering: <scp>X</scp> ‐ray absorption spectroscopy— <scp>XAS</scp> , <scp>XANES</scp> , <scp>EXAFS</scp>
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 Although X‐ray absorption spectroscopy (XAS) was conceived in the early 20th century, it took 60 years after the advent of synchrotrons for researchers to exploit its tremendous potential. Counterintuitively, researchers are now developing bench type polychromatic X‐ray sources that are less brilliant to measure catalyst stability and work with toxic substances. XAS measures the absorption spectra of electrons that X‐rays eject from the tightly bound core electrons to the continuum. The spectrum from 10 to 150 eV (kinetic energy of the photoelectrons) above the chemical potential—binding energy of core electrons—identifies oxidation state and band occupancy (X‐ray absorption near edge structure, XANES), while higher energies in the spectrum relate to local atomic structure like coordination number and distance, Debye‐Waller factor, and inner potential correction (extended X‐ray absorption fine structure, EXAFS). Combining XAS with complementary spectroscopic techniques like Raman, Fourier transform infrared (FTIR), X‐ray photoelectron spectroscopy (XPS), and electron paramagnetic resonance (EPR) elucidates the nature of the chemical bonds at the catalyst surface to better understand reaction mechanisms and intermediates. Because synchrotrons continue to be the light source of choice for most researchers, the number of articles Web of Science indexes per year has grown from 1000 in 1991 to 1700 in 2020. Material scientists and physical chemists publish an order of magnitude articles more than chemical engineers. Based on a bibliometric analysis, the research comprises five clusters centred around: electronic and optical properties, oxidation and hydrogenation catalysis, complementary analytical techniques like FTIR, nanoparticles and electrocatalysis, and iron, metals, and complexes.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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