Potential for optical DNA biosensors and biochips based on a GaAs substrates
Bibliographic record
Abstract
Biosensors and biochips can determine the presence of nucleic acid sequences in a test sample through fluorescence detection of hybridization between an immobilized nucleic acid (probe) and a nucleic acid in a test sample (target). The finding that the control of the environment of immobilized single-stranded probe molecules on fused silica surfaces can be used to tune selectivity to facilitate detection of even single base pair mismatches provides opportunities for design of novel biochips. We have typically used silane coupling agents to activate silicon and silicate surfaces for subsequent immobilization of biomolecules for development of optical biosensors. A self-assembled immobilization providing good structural order would be preferred. Studies have been done using thiol-terminated reagents for assembly of oligonucleotides on GaAs substrates. The spacing of Ga and As can be controlled across a surface, and in turn provides a template to control the density of self-assembled oliogonucleotide. Initial experimental work has begun using homogeneous GaAs surfaces, and the homogeneity and surface morphology of immobilized oligonucleotide films grown onto GaAs has been characterized by atomic force microscopy (AFM) and fluorescence methods. Cycles of hybridization and denaturation suggest that the GaAs provides a surface that is stable to loss of immobilized oligonucleotide, but that efforts to protect from non-selective adsorption are essential. Data suggested that the films were of monolayer thickness, and that it was possible to induce the presence of nodules of approximately 10-50 nm in diameter.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".