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Record W4400147126 · doi:10.1021/acsanm.4c01699

Nanodiamond Emulsions for Enhanced Quantum Sensing and Click-Chemistry Conjugation

2024· article· en· W4400147126 on OpenAlexfundno aff
Henry J. Shulevitz, Ahmad Amirshaghaghi, Mathieu Ouellet, Caroline Brustoloni, Shengsong Yang, Jonah J. Ng, Tzu‐Yung Huang, Davit Jishkariani, Christopher B. Murray, Andrew Tsourkas, Cherie R. Kagan, Lee C. Bassett

Bibliographic record

VenueACS Applied Nano Materials · 2024
Typearticle
Languageen
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsnot available
FundersDivision of Materials ResearchNational Institute of Biomedical Imaging and BioengineeringNatural Sciences and Engineering Research Council of CanadaDivision of Engineering Education and CentersGrainger Foundation
KeywordsNanodiamondClick chemistryNanotechnologyChemistryCombinatorial chemistryMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Nanodiamonds containing nitrogen-vacancy (NV) centers can serve as colloidal quantum sensors of local fields in biological and chemical environments. However, nanodiamond surfaces are challenging to modify without degrading their colloidal stability or the NV center’s optical and spin properties. We present a simple and general method to coat nanodiamonds with a thin emulsion layer that preserves their quantum features, maintains their colloidal stability, and provides functional groups for subsequent cross-linking and click-chemistry conjugation reactions. To demonstrate this technique, we decorated nanodiamonds with combinations of carboxyl- and azide-terminated amphiphiles that enable conjugation using two different strategies. A theoretical model is developed to understand the effect of the emulsion layer on the NV center’s spin lifetime, and T 1 relaxometry is employed to quantify the nanodiamonds’ chemical sensitivity to paramagnetic ions. This general approach to nanodiamond surface functionalization will enable advances in quantum nanomedicine and biological sensing.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.269
Teacher spread0.257 · 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.

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
Published2024
Admission routes1
Has abstractyes

Explore more

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