Ultrasmall TiO<sub><i>x</i></sub> Nanoparticles Rich in Oxygen Vacancies Synthesized through a Simple Strategy for Ultrahigh‐Rate Lithium‐Ion Batteries
Bibliographic record
Abstract
Abstract Ultrasmall particle size (<10 nm) and rich oxygen vacancies are two sought‐after characteristics for titanium dioxide (TiO2) to achieve high performance, namely, high rate and high storage capacity, when being used as an anode in lithium‐ion batteries (LIBs). However, free TiO2 particles simultaneously possessing both characteristics have not been reported, owing to the synthetic challenges. In this study, we report novel TiO2 nanoparticles with ultrasmall size (ca. 5–8 nm) as well as rich oxygen vacancies synthesized through a simple strategy. Specifically, porous carbon nanoparticles were used to confine the TiO2 precursor in the nanosized pores in the carbon nanoparticles, which were annealed at a high temperature in argon to produce the TiO2 nanoparticles with ultrasmall size and rich oxygen vacancies and subsequently annealed in air to burn away the carbon nanoparticles to afford the so‐called TiOx nanoparticles in a quantitative yield. The obtained anatase TiOx nanoparticles showed an exceptional ultrahigh‐rate lithium storage capability. A record reversible specific capacity of 235 mAh g−1 was achieved at the current density of 0.1 A g−1. Even at an ultrahigh rate of 10 A g−1 (ca. 59 C), it still delivered a specific capacity of 90 mAh g−1, which is five times that of the electrode made with the commercial anatase TiO2 nanoparticles. Furthermore, this electrode also showed an excellent cycling performance with capacity retentions of 87 % and 90 % at high rates of 1 A g−1 and 5 A g−1, respectively, after 1000 cycles. The strategy reported in this work can potentially be a universal method for synthesis of other metal oxides with ultrasmall particle size and rich oxygen vacancies.
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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".