Molecular surface coverage standards by reference‐free GIXRF supporting SERS and SEIRA substrate benchmarking
Bibliographic record
Abstract
Abstract Non‐destructive reference‐free grazing incidence X‐ray fluorescence (RF‐GIXRF) is proposed as a highly effective analytical technique for extracting molecular arrangement density in self‐assembled monolayers. The establishment of surface density standards through RF‐GIXRF impacts various applications, from calibrating laboratory XRF setups to expanding its applicability in materials science, particularly in surface coating scenarios with molecular assemblies. Accurate determination of coverage density is crucial for proper functionalization and interaction, such as in assessing the surface concentration of probes on plasmonic nanostructures. However, limited synchrotron radiation access hinders widespread use, prompting the need for molecular surface density standards, especially for benchmarking substrates for surface‐enhanced Raman and infrared absorption spectroscopies (SERS and SEIRA) as well as associated surface‐enhanced techniques. Using reproducible densities on gold ensures a solid evaluation of the number of molecules contributing to enhanced signals, facilitating comparability across substrates. The research discusses the importance of employing molecular surface density standards for advancing the field of surface‐enhanced spectroscopies, encouraging collaborative efforts in protocol development and benchmarking in surface science.
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".