Effect of Synthesis Parameters on the Electrochemical Properties of High‐Surface‐Area Mesoporous Titanium Oxide with Polypyrrole Nanowires in the Pores
Bibliographic record
Abstract
Abstract This paper describes the synthesis and characterisation of high‐surface‐area mesoporous titanium oxides with polypyrrole nanowires within the pores, and the subsequent variation of synthesis parameters such as polymer‐loading level and pore size to improve performance. These modifications are employed to improve the electron conductivity of the amorphous host and exploit the high internal surface areas of over 800 m2g−1 for potential use as a lithium battery cathode material, once fully optimised, with fast charge‐transfer kinetics expected from the proximity of the vast majority of the redox sites at, or near, the surface of the inner pore walls. A full structural characterisation, in addition to electrochemical assessments, of the composite materials is presented and compared to the pristine mesoporous titanium oxide hosts. The best synthesis conditions were achieved with 5 % polymer loading and the largest pore sized host materials. Excessive polymer loading and smaller pore sizes lead to decreased performance, possibly due to inhibition of Li+ transport. The C18 templated TiO2 composite produced the best capacity retention at 58 % retention, and the C12 composite produced the highest initial capacity of 170 mAh g−1 by using a current density of 1 mAcm−2.
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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 | 0.001 |
| 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".