MétaCan
Menu
Back to cohort
Record W4409161811 · doi:10.1007/s12598-025-03256-4

Accelerating charging and elevating capacity of TiO <sub>2</sub> by interface space charge storage

2025· article· en· W4409161811 on OpenAlexaff
Jiaxiang Sun, Shuhui Liu, Liyan Chen, Dingding Zhu, Haixia Yu, Yize Niu, Leqing Zhang, Qinghao Li, Yan He, Guo‐Xing Miao, Guihuan Chen, Qiang Li

Bibliographic record

VenueRare Metals · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of Waterloo
FundersNational Natural Science Foundation of China
KeywordsInterface (matter)Materials scienceSpace chargeCharge (physics)Space (punctuation)Engineering physicsElectrical engineeringOptoelectronicsNanotechnologyComputer scienceOperating systemPhysicsEngineeringComposite materialElectronNuclear physicsParticle physics

Abstract

fetched live from OpenAlex

Abstract Titanium dioxide (TiO 2 ) is an extremely promising anode material for lithium‐ion batteries due to its low cost, minimal volume change, and extended cycle life. However, its electrochemical performance is severely hindered by inherent issues such as poor ionic and electronic conductivity. Here, we design a dual‐phase conductor Co@TiO 2 , which contributes a synergistic storage mode consisting of a Li‐accepting and an electron‐accepting phase. In situ magnetic characterization and experimental results reveal the space charge storage mechanism in addition to traditional insertion mechanisms. Based on these mechanisms, the specific capacity and rate performance of the Co@TiO 2 electrode have been greatly enhanced. Under a current density of 200 mA·g −1 , the specific capacity of Co@TiO 2 reaches 397.2 mAh·g −1 . Upon increasing the current density to 10 A·g −1 , the electrode still maintains a capacity of 83.1 mAh·g −1 after 900 cycles. This result offers a fresh perspective on the structural design of new anode materials to achieve high energy density.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.016
GPT teacher head0.241
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueRare MetalsSame topicAdvancements in Battery MaterialsFrench-language works237,207