Controlling “chemical nose” biosensor characteristics by modulating gold nanoparticle shape and concentration
Bibliographic record
Abstract
Conventional lock-and-key biosensors often only detect a single pathogen because they incorporate biomolecules with high specificity. “Chemical nose” biosensors are overcoming this limitation and identifying multiple pathogens simultaneously by obtaining a unique set of responses for each pathogen of interest, but the number of pathogens that can be distinguished is limited by the number of responses obtained. Herein, we use a gold nanoparticle-based “chemical nose” to show that changing the shapes of nanoparticles can increase the number of responses available for analysis and expand the types of bacteria that can be identified. Using four shapes of nanoparticles (nanospheres, nanostars, nanocubes, and nanorods), we demonstrate that each shape provides a unique set of responses in the presence of different bacteria, which can be exploited for enhanced specificity of the biosensor. Additionally, the concentration of nanoparticles controls the detection limit of the biosensor, where a lower concentration provides better detection limit. Thus, here we lay a foundation for designing “chemical nose” biosensors and controlling their characteristics using gold nanoparticle morphology and concentration.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".