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Record W4378086241 · doi:10.1021/acs.chemmater.3c00640

N-Heteroacenes as an Organic Gain Medium for Room-Temperature Masers

2023· article· en· W4378086241 on OpenAlexaff
Max Attwood, Xiaotian Xu, Michael Newns, Zhu Meng, Rebecca A. Ingle, Hao Wu, Xi Chen, Weidong Xu, Wern Ng, Temitope T. Abiola, Vasilios G. Stavros, Mark Oxborrow

Bibliographic record

VenueChemistry of Materials · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicElectron Spin Resonance Studies
Canadian institutionsUniversity of Toronto
FundersEngineering and Physical Sciences Research CouncilImperial College LondonUniversity of Warwick
KeywordsIntersystem crossingMaserElectron paramagnetic resonanceTetraceneMuoniumMicrowaveChemical physicsMaterials sciencePhotochemistryChemistryMoleculeOptoelectronicsAtomic physicsSinglet stateHyperfine structureNuclear magnetic resonanceExcited statePhysicsOpticsOrganic chemistry

Abstract

fetched live from OpenAlex

The development of future quantum devices such as the maser, i.e., the microwave analog of the laser, could be well-served by the exploration of chemically tunable organic materials. Current iterations of room-temperature organic solid-state masers are composed of an inert host material that is doped with a spin-active molecule. In this work, we systematically modulated the structure of three nitrogen-substituted tetracene derivatives to augment their photoexcited spin dynamics and then evaluated their potential as novel maser gain media by optical, computational, and electronic paramagnetic resonance (EPR) spectroscopy. To facilitate these investigations, we adopted an organic glass former, 1,3,5-tri(1-naphthyl)benzene to act as a universal host. These chemical modifications impacted the rates of intersystem crossing, triplet spin polarization, triplet decay, and spin-lattice relaxation, leading to significant consequences on the conditions required to surpass the maser threshold.

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 score0.004
Threshold uncertainty score0.694

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 science0.0000.000
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 head0.009
GPT teacher head0.270
Teacher spread0.260 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

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