Directed Assembly of Nanoparticle Threshold‐Selector Arrays
Bibliographic record
Abstract
Abstract The directed assembly of ordered arrays of cubic silver nanoparticles featuring distinct electrical threshold‐switching characteristics is reported. Threshold selectors are key elements for nonvolatile resistive random‐access‐memory architectures, as they suppress sneak path currents in crosspoint arrays. Nanocubes are site‐selectively immobilized on a TiO2‐coated silicon surface via a complementary molecular surface functionalization of nanoparticles and substrate based on a Cu(I)‐catalyzed alkyne‐azide cycloaddition without any physical template. Electrical characterization of individual silver nanocubes by conductive‐probe atomic force microscopy reveals pronounced and reproducible threshold‐switching behavior, featuring ultralow OFF currents below 1 pA, steep turn‐on slopes of <50 mV dec−1 and ON‐OFF ratios in excess of 103. Numerical simulation of Ag‐ion migration dynamics in the TiO2 electrolyte using a kinetic Monte Carlo model supports a switching mechanism based on conductive filament formation from Ag nanoclusters, and their reversible rupture in the low‐voltage regime. Assembled Ag nanocube threshold selectors are proposed for applications in memristive memory architectures, in particular for future highly integrated 3D circuitry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
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".