Synthesis and Modification of TiO<sub>2 </sub>nanostructured Materials for Energy Storage
Bibliographic record
Abstract
Increasing energy demand is inexorably linked to the need for efficient energy storage techniques. For practical applications, it is highly desirable to decrease the size of electrical components and to increase the storage capacities while maintaining power, stability, and charging-discharging speeds. Much focus has been directed towards the development of supercapacitors. These are often fabricated from carbonaceous or metal oxide materials with high surface areas to maximize electrode/electrolyte interactions. The use of nanostructured TiO2 electrodes has been explored for this application due to their low cost, high stability, and highly tunable morphology. Here we present the fabrication of nanostructured TiO2 films via a facile anodic process. Characterization of the films was carried out by scanning electron microscopy, energy-dispersive X-ray spectroscopy, X-ray photoelectron spectroscopy, and X-ray diffraction. Electrochemical reduction of the formed TiO2 film was further performed to increase its capacitance, which was confirmed by cyclic voltammetry and electrochemical impedance spectroscopy. The performance of the symmetric capacitor constructed with the modified TiO2 films will be presented.
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 | 0.000 |
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".