<i>N</i>-Doped Graphene/MXene Nanocomposite as a Temperature-Adaptive Neuromorphic Memristor
Bibliographic record
Abstract
Due to intensive integration and seamless continuous operation, the overheated artificially intelligent (AI) integrated circuit systems will affect the operation system’s effectiveness, stability, and lifetime. Therefore, we proposed a temperature adaptability memristor in the silver nanowires (AgNWs)/nanocomposite/indium–tin-oxide structure in this study. The nanocomposite is the nitrogen-doped graphene/Ti 3 CNT x MXene blend in the polyvinylidene fluoride matrix. The device has been prepared by using heterostructure nanocomposites with a low-cost and facile all-solution method. The device mimicked a series of trained behaviors inherent with biological synapses, including spike-timing-dependent plasticity, paired-pulse facilitation, short-term potentiation/depression, long-term potentiation/depression, and excitatory postsynaptic currents. In addition, the device demonstrated significant self-adaptability to temperature due to the involvement of the homogeneously distributed conductive heterostructure and the filament formation ascribed to the low melting point of AgNWs. The operation of the device shows temperature adaptability owing to the excellent thermal conductivity and small thermal expansion coefficient of nitrogen-doped graphene/Ti 3 CNT x . This finding provides viable strategies to address the critical challenges of deploying AI in different environments, paving the way for the development of more efficient and resilient neuromorphic computing systems.
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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".