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Record W4403761982 · doi:10.1002/anie.202416380

Room‐temperature Magnetocapacitance Spanning 97K Hysteresis in Molecular Material

2024· article· en· W4403761982 on OpenAlexaff
Ling‐Ao Gui, Jiawei Chen, Yifan Zhang, Long‐He Li, Jian‐Rong Li, Zhao‐Bo Hu, Shi‐Yong Zhang, Jinlei Zhang, Zhenyi Zhang, Heng‐Yun Ye, Peng Yan, Jing Ma, You Song

Bibliographic record

VenueAngewandte Chemie International Edition · 2024
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsMinistry of Education and Child Care
FundersNatural Science Foundation of Jiangxi ProvinceNational Natural Science Foundation of China
KeywordsMagnetocapacitanceHysteresisMaterials scienceCondensed matter physicsPhysicsOptoelectronicsMultiferroicsFerroelectricityDielectric

Abstract

fetched live from OpenAlex

Abstract Magnetic capacitor, as a new type of device, has broad application prospects in fields such as magnetic field sensing, magnetic storage, magnetic field control, power electronics and so on. Traditional magnetic capacitors are mostly assembled by magnetic and capacitive materials. Magnetic capacitor made of a single material with intrinsic properties is very rare. This intrinsic property is magnetocapacitance (MC). The studies on MC effect have mainly focused on metal oxides so far. No study was reported in molecular materials. Herein, two complexes: (CETAB)2[CuCl4] (1) and (CETAB)2[CuBr4] (2) (CETAB=(2‐chloroethyl)trimethylammonium) are reported. There exist strong H−Br and Br−Br interactions and other weak interactions in complex 2, so the phase transition energy barrier is high, resulting in the widest thermal hysteresis loop on a molecular level to date. Furthermore, complexes 1 and 2 show large MC parameters of 0.247 and 1.614, respectively, which is the first time to observe MC effect in molecular material.

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 score0.002
Threshold uncertainty score0.008

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)0.0020.000

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 head0.009
GPT teacher head0.240
Teacher spread0.231 · 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

Citations5
Published2024
Admission routes1
Has abstractyes

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