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Record W4310430811 · doi:10.1021/acs.nanolett.2c01240

Biobased Self-Growing Approach toward Tailored, Integrated High-Performance Flexible Lithium-Ion Battery

2022· article· en· W4310430811 on OpenAlexaff
Ruohong Ke, Leilei Du, Bing Han, Hongli Xu, Hong Meng, Hongbo Zeng, Zijian Zheng, Yonghong Deng

Bibliographic record

VenueNano Letters · 2022
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of ChinaGuangdong Science and Technology DepartmentScience and Technology Foundation of Shenzhen City
KeywordsMaterials scienceSeparator (oil production)NanotechnologyFlexibility (engineering)Bacterial celluloseLithium-ion batteryElectrochemistryFlatteningBattery (electricity)ElectrodeBendingScalabilityCelluloseComputer scienceComposite materialChemical engineeringChemistryEngineeringPhysics

Abstract

fetched live from OpenAlex

Here we present an innovative, universal, scalable, and straightforward strategy for cultivating a resilient, flexible lithium-ion battery (LIB) based on the bacterial-based self-growing approach. The electrodes and separator layers are integrated intrinsically into one unity of sandwich bacterial cellulose integrated film (SBCIF), with various active material combinations and tailored mechanical properties. The flexible LIB thereof showcases prominent deformation tolerance and multistage foldability due to the unique self-generated wavy-like structure. The LTO|LFP (Li4Ti5O12 and LiFePO4) SBCIF-based flexible LIB demonstrates reliable long-term electrochemical stability with high flexibility, by exhibiting a high capacity retention (>95%) after 500 cycles at 1C/1C after experiencing a 10 000 bending/flattening treatment. The LTO|LFP SBCIF battery subjected to a simultaneous bending/flattening and cycling experiment shows an extraordinary capacity retention rate (>68%) after 200 cycles at 1C/1C. The biobased self-growing approach offers an exciting and promising pathway toward the tailored, integrated high-performance flexible LIBs.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.006
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.017
GPT teacher head0.206
Teacher spread0.189 · 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 teacher head, not a consensus.

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

Citations13
Published2022
Admission routes1
Has abstractyes

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