A Battery-Like Self-Selecting Biomemristor from Earth-Abundant Natural Biomaterials
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Using the earth-abundant natural biomaterials to manufacture functional electronic devices meets the sustainable requirement of green electronics, especially for the practical application of memristors in data storage and neuromorphic computing. However, the sneak currents flowing though the unselected cells in a large-scale cross-bar memristor array is one of the major problems which need to be tackled. The self-selecting memristors can solve the problem to develop compact and concise integrated circuits. Here, a sustainable natural biomaterial (anthocyanin, C15H11O6) extracted from plant tissue is demonstrated for ions and electron transport. The capacitive-coupled memristive behavior of as-prepared bioelectronic device can be significantly modulated by diethylmethyl(2-methoxyethyl)ammoium bis(trifluoromethylsulfonyl)imide (DEME-TFSI) ionic liquid (IL). Furthermore, graphene was inserted into biomaterial matrix to manipulate the memristive effects by graphene protonation. This results in a battery-like self-selective memristive effect. This phenomenon is explained by a physical model and density functional theory (DFT) based first-principles calculations. Finally, the self-selective behavior was applied in 0T-1R array configuration, which indicates the battery-like self-selecting biomemristor has potential applications in the brain-inspired computing, data storage systems, and high-density device integration.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it