Self-assembly of biomolecules at surfaces characterized by NEXAFS
Bibliographic record
Abstract
Surface science has made great strides towards tailoring surface properties via self-assembly of nanoscale molecular adsorbates. It is now possible to functionalize surfaces with complex biomolecules such as DNA and proteins. This brief overview shows how NEXAFS (near edge X-ray absorption fine structure spectroscopy) can be used to characterize the assembly of biological molecules at surfaces in atom- and orbital-specific fashion. To illustrate the range of applications, we begin with simple self-assembled monolayers (SAMs), proceed to SAMs with customized terminal groups, and finish with DNA oligonucleotides and Ribonuclease A, a small protein containing 124 amino acids. The N 1s absorption edge is particularly useful for characterizing DNA and proteins because it selectively interrogates the π* orbitals in nucleobases and the peptide bonds in proteins. Information about the orientation of molecular orbitals is obtained from the polarization dependence. Quantitative NEXAFS models explain the polarization dependence in terms of molecular orientation and structure.Key words: NEXAFS, bio-interfaces, ribonuclease A, immobilization, orientation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".