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Record W4393754647 · doi:10.1103/bhyh-53np

Extending the Self-Discharge Time of Dicke Quantum Batteries Using Molecular Triplets

2025· preprint· en· W4393754647 on OpenAlexfundno aff
Daniel J. Tibben, Enrico Della Gaspera, Joel van Embden, Philipp Reineck, James Q. Quach, Francesco Campaioli, Daniel E. Gómez

Bibliographic record

VenuePRX Energy · 2025
Typepreprint
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsnot available
FundersAustralian Research CouncilHORIZON EUROPE Framework ProgrammeRMIT UniversityEuropean CommissionOntario Ministry of Natural Resources and ForestryAustralian National Fabrication Facility
KeywordsQuantumPhysicsQuantum mechanics

Abstract

fetched live from OpenAlex

Quantum batteries, quantum systems for energy storage, have gained interest due to their potential scalable charging power density. A quantum battery proposal based on the Dicke model has been explored using organic microcavities, which enable a cavity-enhanced energy-transfer process called superabsorption. However, energy-storage lifetime in these devices is limited by fast radiative emission losses, worsened by superradiance. Here, we demonstrate a promising approach to extend the energy-storage lifetime of Dicke quantum batteries using molecular triplet states. We examine a type of multilayer microcavity where an active absorption layer transfers energy to the molecular triplets of a storage layer, identifying two regimes based on exciton-polariton resonances. We tested one of these mechanisms by fabricating and characterizing five devices across a triplet-polariton resonance, showing that triplet population is maximized when the lower polariton and triplet state are isoenergetic. We found that one of these devices can store energy for 40.3 <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"> <a:mo>±</a:mo> </a:math> 0.4 <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" display="inline"> <c:mi>μ</c:mi> <c:mrow> <c:mrow> <c:mi mathvariant="normal">s</c:mi> </c:mrow> </c:mrow> </c:math> , a <f:math xmlns:f="http://www.w3.org/1998/Math/MathML" display="inline"> <f:msup> <f:mn>10</f:mn> <f:mn>3</f:mn> </f:msup> </f:math> -fold increase in storage time compared to previous demonstrations. We conclude by discussing potential optimization outlooks for this class of devices.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.803

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.010
GPT teacher head0.249
Teacher spread0.240 · 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

Citations9
Published2025
Admission routes1
Has abstractyes

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