Electrolyte-gated charge transport in molecularly linked gold nanoparticle films: The transition from a Mott insulator to an exotic metal with strong electron-electron interactions
Bibliographic record
Abstract
Strong electron-electron interactions experienced by electrons as they delocalize are widely believed to play a key role in a range of remarkable phenomena such as high Tc superconductivity, colossal magnetoresistance, and others. Strongly correlated electrons are often described by the Hubbard model, which is the simplest description of a correlated system and captures important gross features of phase diagrams of strongly correlated materials. However, open challenges in this field include experimentally mapping correlated electron phenomena beyond those captured by the Hubbard model, and extending the model accordingly. Here we use electrolyte gating to study a metal-insulator transition (MIT) in a new class of strongly correlated material, namely, nanostructured materials, using 1,4-butanedithiol-linked Au nanoparticle films (NPFs) as an example. Electrolyte gating provides a means for tuning the chemical potential of the materials over a wide range, without significantly modifying film morphology. On the insulating side of the transition, we observe Efros-Shklovskii variable range hopping and a soft Coulomb gap, evidencing the importance of Coulomb barriers. On the metallic side of the transition, we observe signatures of strong disorder mediated electron-electron correlations. Gating films near MIT also reveal a zero-bias conductance peak, which we attribute to a resonance at the Fermi level predicted by the Hubbard and Anderson impurity models when electrons delocalize and experience strong Coulomb electron-electron interactions. This study shows that by enabling large changes in carrier density, electrolyte gating of Au NPFs is a powerful means for tuning through the Hubbard MIT in NPFs. By revealing the range of behaviours that strongly correlated electrons can exhibit, this platform can guide the development of an improved understanding of correlated materials.
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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".