Gold surface nanostructuring for separation and sensing of biomolecules
Bibliographic record
Abstract
Detecting biomolecules in physiological environments is critical to health care and environmental monitoring. In this work, we study and use gold surfaces for biomolecule detection while incorporating nanoscale components—specifically, self-assembled monolayers (SAMs) of alkanethiols and gold nanostructured shells—with the goal of improving biomolecule detection methods. Using SAMs to functionalize gold surfaces can offer control over biomolecule binding density and orientation while still keeping the biomolecules near the sensing surface. Using surface IR spectroscopy, x-ray photoelectron spectroscopy (XPS), and density functional theory (DFT) modeling, we found that SAMs of short-chain and long-chain amine-terminated alkanethiols on gold had different sulphur binding environments. We also found that protein binding and recognition on the two different SAMs varied with SAM chain length and was also influenced by the presence of a cross-linker. In the second part of this work, we synthesized gold nanostructured shells on magnetic particles for combined separation and detection of biomolecules. We demonstrated their use as substrates for surface-enhanced Raman spectroscopy (SERS) As a proof-of-concept, we demonstrated the use of these particles to detect oligonucleotide binding and hybridization with SERS using a Raman-tagged oligonucleotide hairpin probe.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".