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Record W4410306581 · doi:10.26434/chemrxiv-2025-f8wcj

Microwave-Free Nuclear Spin Hyperpolarization through Photo-CIDNP in Static and Rotating Solids

2025· preprint· en· W4410306581 on OpenAlexfundno aff
Sajith V. Sadasivan, Asif Equbal

Bibliographic record

VenueChemRxiv · 2025
Typepreprint
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsnot available
FundersTamkeenYork UniversityNew York University Abu Dhabi
KeywordsHyperpolarization (physics)CIDNPMicrowaveSpin (aerodynamics)Polarization (electrochemistry)PhysicsOptoelectronicsChemistryMaterials scienceNuclear magnetic resonanceQuantum mechanicsNuclear magnetic resonance spectroscopyPhysical chemistryThermodynamics

Abstract

fetched live from OpenAlex

This study advances the theoretical foundation of photo-chemically induced dynamic nuclear polarization (photo- CIDNP)-a powerful mechanism for enhancing nuclear spin sensitivity without microwave irradiation. Using an operator-based effective Hamiltonian approach, we derive precise resonance matching conditions and identify key dipolar scaling factors governing the photo-CIDNP Hamiltonian under both static and magic-angle spinning (MAS) conditions. Our analytical formulation of coherent evolution of photoexcited singlet state exhibits strong agreement with numerical simulations, reinforcing the validity of our theoretical framework. By unraveling the intricate interplay of spin parameters in the radical-pair mechanism, our findings provide critical insights for optimizing photo-CIDNP efficiency and guiding the rational design of tailored molecular systems. The ability to develop highly efficient photo- CIDNP sensitizers marks a crucial step toward harnessing hyperpolarized NMR and MRI, paving the way for next- generation advancements in biomedical imaging and materials science

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

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 studies0.0000.001
Scholarly communication0.0000.001
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 head0.018
GPT teacher head0.294
Teacher spread0.276 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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