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Improving the multicaloric properties of Pb(Fe0.5Nb0.5)O3 by controlling the sintering conditions and doping with manganese

2019· article· en· W2947666360 on OpenAlexaff
Uroš Prah, Tadej Rojac, Magdalena Wencka, Mirela Dragomir, Andraž Bradeško, Andreja Benčan, Rachel Sherbondy, Geoff L. Brennecka, Zdravko Kutnjak, Barbara Malič, Hana Uršič

Bibliographic record

VenueJournal of the European Ceramic Society · 2019
Typearticle
Languageen
FieldMaterials Science
TopicFerroelectric and Piezoelectric Materials
Canadian institutionsMcMaster University
FundersJavna Agencija za Raziskovalno Dejavnost RS
KeywordsMagnetic refrigerationMaterials scienceManganeseSinteringDopingCeramicJoule heatingElectrocaloric effectElectrical resistivity and conductivityAnalytical Chemistry (journal)Composite materialMagnetizationMetallurgyMagnetic fieldOptoelectronicsElectrical engineeringFerroelectricity

Abstract

fetched live from OpenAlex

Designing multicaloric single-phase materials with combined electro- and magnetocaloric effects is still at its initial stage and presents a number of challenges. One of the main challenges encountered so far is to reduce the excessive electrical conductivity, which leads to the appearance of Joule heating that might completely degrade the electrocaloric response. In this work, multicaloric Pb(Fe0.5Nb0.5)O3 material was successfully prepared exhibiting pronounced electrocaloric effect above room temperature and maximum magnetocaloric effect at cryogenic temperature. The conductivity was suppressed by controlling the sintering temperature. The ceramic sintered at 1000 °C exhibits maximum electrocaloric effective cooling of 0.88 °C at 28 °C and maximum magnetocaloric effect of 0.14 °C at −271 °C. The caloric properties can be further improved by doping Pb(Fe0.5Nb0.5)O3 with manganese. In comparison to the undoped sample, Pb(Fe0.5Nb0.5)O3 doped with 0.5 mol% of manganese exhibits three times higher maxima of electrocaloric effective cooling (2.47 °C at 80 °C) and magnetocaloric temperature change (0.44 °C at −271 °C).

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)0.002
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.257

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
Research integrity0.0000.000
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 head0.189
Teacher spread0.181 · 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

Citations14
Published2019
Admission routes1
Has abstractyes

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