Quasilocalized hopping in molecularly linked Au nanoparticle arrays near the metal-insulator transition
Bibliographic record
Abstract
We have investigated the temperature dependence of the conductance of 1,4-butane dithiol linked Au nanoparticle films from $2\phantom{\rule{0.3em}{0ex}}\mathrm{K}\phantom{\rule{0.3em}{0ex}}\text{to}\phantom{\rule{0.3em}{0ex}}300\phantom{\rule{0.3em}{0ex}}\mathrm{K}$. At low temperatures $(T<10\phantom{\rule{0.3em}{0ex}}\mathrm{K})$, conductance becomes independent of temperature and exhibits strong nonlinearity with voltage, which we attribute to tunneling. At higher temperatures $(T>20\phantom{\rule{0.3em}{0ex}}\mathrm{K})$, the conductance behaves as $g\ensuremath{\propto}\mathrm{exp}[\ensuremath{-}{({T}_{0}∕T)}^{1∕2}]$. Qualitatively, this is consistent with an Efros-Shklovskii variable range hopping model based on a competition between Coulombic and intercluster tunneling processes. However, we find that hopping distances are too large ($62--720\phantom{\rule{0.3em}{0ex}}\mathrm{nm}$ at $100\phantom{\rule{0.3em}{0ex}}\mathrm{K}$) to be consistent with tunneling between clusters and tend to scale with cluster size. We propose a modified, ``quasilocalized hopping'' model based on competition between single-electron cluster charging and intracluster electron backscattering to explain this temperature dependence.
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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) | 0.001 | 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".