Conductivity control of as-grown branched indium tin oxide nanowire networks
Bibliographic record
Abstract
Branched indium tin oxide (ITO) nanowire networks are promising candidates for transparent conductive oxide applications, such as optoelectronic electrodes, due to their high porosity. However, these branched networks also present new challenges in assessing conductivity. Conventional four-point probe techniques cannot separate the effect of porosity on the long-range conductivity from the intrinsic material conductivity. Here we compare the average nanoscale conductivity within the film measured by terahertz time-domain spectroscopy (THz-TDS) to the film conductivity measured by four-point probe in a branched ITO nanowire network. Both techniques report conductivity increases with deposition flux rate from 0.5 to 3.0 nm s(-1), achieving a maximum of ~ 10 (Ω cm)(-1). Modeling the THz-TDS conductivity data using the Drude-Smith model allows us to distinguish between conductivity increases resulting from morphological changes and those resulting from the intrinsic properties of the ITO. In particular, the intrinsic material conductivity within the nanowires can be extracted, and is found to reach a maximum of ~ 3000 (Ω cm)(-1), comparable to bulk ITO. To determine the mechanism responsible for increasing conductivity with flux rate, we characterize dopant concentration and morphological changes (i.e., to branching behavior, nanowire diameter and nucleation layers). We propose that changes in the electron density, primarily due to changes in O-vacancy concentration at different flux rates, are responsible for the observed conductivity increase. This understanding will assist balancing structural and conductivity requirements in applications of transparent conductive oxide networks.
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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 | 0.000 | 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 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".