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Record W2925023779 · doi:10.48550/arxiv.1903.10791

Photoswitchable Single-Walled Carbon Nanotubes for Super-Resolution\n Microscopy in the Near-Infrared

2019· preprint· W2925023779 on OpenAlexaff
Antoine G. Godin, Antonio Setaro, Morgane Gandil, Rainer Haag, Mohsen Adeli, Stephanie Reich, Laurent Cognet

Bibliographic record

VenuearXiv (Cornell University) · 2019
Typepreprint
Language
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsInstitut Universitaire en Santé Mentale de Québec
Fundersnot available
KeywordsCarbon nanotubeInfraredInfrared microscopyMicroscopyResolution (logic)Materials scienceNanotechnologyOpticsPhysicsComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

The design of single-molecule photoswitchable emitters was the first milestone toward the advent of single-molecule localization microscopy that sets a new paradigm in the field of optical imaging. Several photoswitchable emitters have been developed but they all fluoresce in the visible or far-red ranges, missing the desirable near-infrared window where biological tissues are most transparent. Moreover, photocontrol of individual emitters in the near-infrared would be highly desirable for elementary optical molecular switches or information storage elements since most communication data transfer protocols are established in this spectral range. Here we introduce a novel type of hybrid nanomaterials consisting of single-wall carbon nanotubes covalently functionalized with photo-switching molecules that are used to control the intrinsic luminescence of the single nanotubes in the near-infrared (beyond 1 $\mu$m). We provide proof-of-concept of localization microscopy based on these bright photoswitchable near-infrared emitters.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.058
GPT teacher head0.208
Teacher spread0.150 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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Same venuearXiv (Cornell University)Same topicCarbon Nanotubes in CompositesFrench-language works237,207