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Record W2903158138 · doi:10.1002/adma.201806993

Amorphous Quantum Nanomaterials

2018· article· en· W2903158138 on OpenAlexaff
Ferdinand F. E. Kohle, Joshua A. Hinckley, Songying Li, Nikhil Dhawan, William P. Katt, Jacob A. Erstling, Ulrike Werner‐Zwanziger, Josef W. Zwanziger, Richard A. Cerione, Ulrich Wiesner

Bibliographic record

VenueAdvanced Materials · 2018
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsDalhousie University
FundersNational Institute of General Medical SciencesBasic Energy SciencesMaterials Research Science and Engineering Center, Harvard UniversityCornell Center for Materials ResearchAlfred P. Sloan FoundationU.S. Department of EnergyDivision of Materials ResearchOffice of ScienceNational Cancer InstituteNational Institutes of HealthNational Science Foundation
KeywordsAmorphous solidMaterials scienceNanomaterialsDelocalized electronNanotechnologyQuantumChemical physicsSpectroscopyCrystallographyPhysicsQuantum mechanics

Abstract

fetched live from OpenAlex

In quantum materials, macroscopic behavior is governed in nontrivial ways by quantum phenomena. This is usually achieved by exquisite control over atomic positions in crystalline solids. Here, it is demonstrated that the use of disordered glassy materials provides unique opportunities to tailor quantum material properties. By borrowing ideas from single-molecule spectroscopy, single delocalized π-electron dye systems are isolated in relatively rigid ultrasmall (<10 nm diameter) amorphous silica nanoparticles. It is demonstrated that chemically tuning the local amorphous silica environment around the dye over a range of compositions enables exquisite control over dye quantum behavior, leading to efficient probes for photodynamic therapy (PDT) and stochastic optical reconstruction microscopy (STORM). The results suggest that efficient fine-tuning of light-induced quantum behavior mediated via effects like spin-orbit coupling can be effectively achieved by systematically varying averaged local environments in glassy amorphous materials as opposed to tailoring well-defined neighboring atomic lattice positions in crystalline solids. The resulting nanoprobes exhibit features proven to enable clinical translation.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.045
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0180.008

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 head0.016
GPT teacher head0.248
Teacher spread0.232 · 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; both teacher heads agree on what is shown here.

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

Citations19
Published2018
Admission routes1
Has abstractyes

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