Large emergent optoelectronic enhancement in molecularly cross-linked gold nanoparticle nanosheets
Bibliographic record
Abstract
Abstract A central goal in molecular electronics and optoelectronics is to translate tailorable molecular properties to larger materials and to the device level. Here, we present a method to fabricate molecularly cross-linked, self-assembled 2D nanoparticle sheets (X-NS). Our method extends a Langmuir approach of self-assembling gold nanoparticle (NP) arrays at an air-water interface by replacing the liquid sub-phase to an organic solvent to enable cross-linking with organic molecules, and then draining the sub-phase to deposit films. Remarkably, X-NS comprising conjugated oligophenylene dithiol cross-linkers (HS-(C6H4)n-SH, 1 ≤ n ≤ 3) exhibit increasing conductance with molecule length, ~6 orders of magnitude enhancement in UV-Vis extinction coefficients, and photoconductivity with molecule vs. NP contributions varying depending on the excitation wavelength. Finite difference time domain (FDTD) analyses and control measurements indicate that these effects can be modeled provided the local complex dielectric constant is strongly modified upon cross-linking. This suggests quantum hybridization at a molecule–band (q-MB) level. Given the vast number of molecules and nano-building blocks available, X-NS have potential to significantly increase the range of available 2D nanosheets and associated quantum properties.
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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".