Nanoporous Gold as a VOC Sensor, Based on Nanoscale Electrical Phenomena and Convolutional Neural Networks
Bibliographic record
Abstract
Volatile organic compounds (VOCs) are prevalent in daily life, from the lab environment to industrial applications, providing tremendous functionality but also posing significant health risk. Moreover, individual VOCs have individual risks associated with them, making classification and sensing of a broad range of VOCs important. This work details the application of electrochemically dealloyed nanoporous gold (NPG) as a VOC sensor through measurements of the complex electrical frequency response of NPG. By leveraging the effects of adsorption and capillary condensation on the electrical properties of NPG itself, classification and regression is possible. Due to the complex nonlinearities, classification and regression are done through the use of a convolutional neural network. This work also establishes key strategies for improving the performance of NPG, both in sensitivity and selectivity. This is achieved by tuning the electrochemical dealloying process through manipulations of the starting alloy and through functionalization with 1-dodecanethiol.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".