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Record W4280553732 · doi:10.1038/s41467-022-30541-y

Exceptionally high work density of a ferroelectric dynamic organic crystal around room temperature

2022· article· en· W4280553732 on OpenAlexfundno aff
Durga Prasad Karothu, Rodrigo Cezar de Campos Ferreira, Ghada Dushaq, Ejaz Ahmed, Luca Catalano, Jad Mahmoud Halabi, Zainab Alhaddad, Ibrahim Tahir, Liang Li, Sharmarke Mohamed, Mahmoud Rasras, Pancě Naumov

Bibliographic record

VenueNature Communications · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsnot available
FundersYork UniversityKhalifa University of Science, Technology and ResearchNew York University Abu Dhabi
KeywordsMaterials scienceFerroelectricityCapacitorActuatorDielectricThermistorWork (physics)Ionic bondingCrystal (programming language)CeramicOptoelectronicsNanotechnologyComposite materialIonVoltageElectrical engineeringMechanical engineeringChemistryOrganic chemistryComputer science

Abstract

fetched live from OpenAlex

Dynamic organic crystals are rapidly gaining traction as a new class of smart materials for energy conversion, however, they are only capable of very small strokes (<12%) and most of them operate through energetically cost-prohibitive processes at high temperatures. We report on the exceptional performance of an organic actuating material with exceedingly large stroke that can reversibly convert energy into work around room temperature. When transitioning at 295-305 K on heating and at 265-275 K on cooling the ferroelectric crystals of guanidinium nitrate exert a linear stroke of 51%, the highest value observed with a reversible operation of an organic single crystal actuator. Their maximum force density is higher than electric cylinders, ceramic piezoactuators, and electrostatic actuators, and their work capacity is close to that of thermal actuators. This work demonstrates the hitherto untapped potential of ionic organic crystals for applications such as light-weight capacitors, dielectrics, ferroelectric tunnel junctions, and thermistors.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.787
Threshold uncertainty score0.625

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.228
Teacher spread0.220 · 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.

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

Citations61
Published2022
Admission routes1
Has abstractyes

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