MétaCan
Menu
Back to cohort
Record W4285590465 · doi:10.1002/anie.202207711

Solid–Liquid Interfacial Coordination Chemistry Enables High‐Capacity Ammonium Storage in Amorphous Manganese Phosphate

2022· article· en· W4285590465 on OpenAlexaff
Duo Yang, Yu Song, Mingyue Zhang, Zengming Qin, Jie Liu, Xiaoxia Liu

Bibliographic record

VenueAngewandte Chemie International Edition · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsIron Ore Company (Canada)
FundersFundamental Research Funds for the Central UniversitiesHigher Education Discipline Innovation ProjectNational Natural Science Foundation of China
KeywordsElectrolyteAqueous solutionAmmoniumElectrochemistryManganeseInorganic chemistryChemistryIonAmorphous solidPhosphateElectrodeOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract Ammonium (NH4+) ion as charge carrier is attracting attention in aqueous batteries. Yet, most NH4+ host materials are still limited by the relatively low capacities. Here, we fabricated a manganese phosphate (MP‐20) for NH4+ ion storage. MP‐20 displays a high capacity of 299.6 mAh g−1 at 1 A g−1 in ammonium acetate (NH4Ac) electrolyte, outperforming other reported NH4+ host materials. Spectroscopy studies suggest a new NH4+/H+ co‐insertion mechanism. We surprisingly discover that the NH4Ac electrolyte plays an important role in improving the charge storage capability of the materials. Experimental and computational results indicate acetate ions can form coordination bonds with the Mn atoms, tailoring the electronic structure of the Mn atoms and the surrounding O atoms, and therefore facilitating the NH4+ storage process. Our findings provide a new NH4+ host material and propose the important role of the electrolyte‐electrode coordination effect in aqueous ammonium batteries.

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 score0.003

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 head0.013
GPT teacher head0.255
Teacher spread0.242 · 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

Citations97
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueAngewandte Chemie International EditionSame topicAdvanced battery technologies researchFrench-language works237,207