Sensing Responses Based on Transfer Characteristics of InAs Nanowire Field-Effect Transistors
Bibliographic record
Abstract
Nanowire-based field-effect transistors (FETs) have demonstrated considerable promise for a new generation of chemical and biological sensors. Indium arsenide (InAs), by virtue of its high electron mobility and intrinsic surface accumulation layer of electrons, holds properties beneficial for creating high performance sensors that can be used in applications such as point-of-care testing for patients diagnosed with chronic diseases. Here, we propose devices based on a parallel configuration of InAs nanowires and investigate sensor responses from measurements of conductance over time and FET characteristics. The devices were tested in controlled concentrations of vapour containing acetic acid, 2-butanone and methanol. After adsorption of analyte molecules, trends in the transient current and transfer curves are correlated with the nature of the surface interaction. Specifically, we observed proportionality between acetic acid concentration and relative conductance change, off current and surface charge density extracted from subthreshold behaviour. We suggest the origin of the sensing response to acetic acid as a two-part, reversible acid-base and redox reaction between acetic acid, InAs and its native oxide that forms slow, donor-like states at the nanowire surface. We further describe a simple model that is able to distinguish the occurrence of physical versus chemical adsorption by comparing the values of the extracted surface charge density. These studies demonstrate that InAs nanowires can produce a multitude of sensor responses for the purpose of developing next generation, multi-dimensional sensor applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".