Nanostructure Enabled Memristor and Supercapacitor
Bibliographic record
Abstract
The composition of conventional circuits is based on four basic elements: resistors, capacitors, inductors, and memristors. Among them, resistors and inductors have been widely studied. Memristor has a desirable application prospect in information storage, artificial synapse, neural network, and artificial intelligence. Capacitors, specifically supercapacitors, have shown better performance, higher capacitance, and efficiency than traditional batteries and capacitors. With the development of technology, more and more electronic products have come into people's lives, and the application of the Internet of Things (IoT) has become more and more widespread. Supercapacitors as energy storage devices and memristors as data storage & computing device play crucial roles in the development of IoT. \nThe memristor is usually considered a non-linear resistor with a memory function. The more widely accepted mechanism is the formation of a conductive filament, which transforms the resistor from a linear resistive state to a resistive-capacitive coupled state and then to a pure memory state. However, there is still a lack of theoretical knowledge on the mechanism of memristors. Is it starting as a linear resistance? This is a problem worth exploring in the development of memristive devices. \nIn this thesis, a silver (Ag)/Prussian blue (PB)/In-doped Tin Oxide (ITO) structure shows a unique glucose-controlled transition from a linear resistive state to a capacitance-coupled storage effect and finally to a pure memristive storage effect. Unlike other methods of controlling circuit elements, glucose, as a new factor, is a very effective and direct method of controlling circuit elements. Ion transport recombination and redox reactions under bias in lead layers are controlled by glucose to form conductive filaments in the switching layer PB. This device further explores the mechanism of conduction filament and provides a promising application in complex integrated sensor and artificial intelligence applications. \nSupercapacitors have been extensively studied for their excellent rate performance and cycling stability. The electrode material is the main factor that determines the performance of the capacitor. Studies have shown that electrode materials' structure, surface area, and morphology are essential factors in determining electrochemical performance. However, constructing supercapacitors with high energy density, long cycle life, and high capacitance remains challenging. Therefore, to solve the current problems, it is crucial to research the electrode material with better performance. \nIn this thesis, magnetron sputtering techniques were first introduced to construct MnO2 and V2O5 decorated carbon-based electrodes. First, spin coating the carbon nanofibers on the carbon cloth substrate and then sputter the MnO2 and V2O5 multilayer. The addition of carbon nanofibers increases the electrical conductivity of carbon cloth and improves the electron transfer rate, while the double carbon core-shell structure improves the reaction site for metal oxide deposition. Electrodes with CC/CNFs/MnO2/V2O5/MnO2/V2O5/MnO2/V2O5/MnO2 (MVMVMVM) structure show the best electrochemical performance. At a scan rate of 5 mV/s, the capacitance is increased by a factor of nearly 10 compared to the carbon cloth electrode based on CNFs, with a capacitance of 90 mF/cm3. As for the cell stability, after 2000 cycles of CV loops, the capacitance extension remains stable at 120%. Due to the nanostructured multilayer MnO2/V2O5, the redox ion reaction can pass through the surface to the interlayer, exhibiting partial to full activation. This study constructs the supercapacitor electric double-layer pseudocapacitive electrode interface from the atomic scale, which provides an essential reference and impetus for the development of supercapacitors.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".