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Polymeric biomaterials for biophotonic applications

2018· review· en· W2887262221 on OpenAlexaff
Dingying Shan, Ethan Gerhard, Chenji Zhang, J.W. Tierney, D. Z. Xie, Zhiwen Liu, Jian Yang

Bibliographic record

VenueBioactive Materials · 2018
Typereview
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsAssumption University
FundersNational Institute of Biomedical Imaging and BioengineeringNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Cancer InstituteNational Institutes of Health
KeywordsMaterials scienceBiocompatible materialNanotechnologyOptical materialsOptical imagingFabricationBiomedical engineeringOptoelectronicsOpticsEngineering

Abstract

fetched live from OpenAlex

With the growing importance of optical techniques in medical diagnosis and treatment, there exists a pressing need to develop and optimize materials platform for biophotonic applications. Particularly, the design of biocompatible and biodegradable materials with desired optical, mechanical, chemical, and biological properties is required to enable clinically relevant biophotonic devices for translating in vitro optical techniques into in situ and in vivo use. This technological trend propels the development of natural and synthetic polymeric biomaterials to replace traditional brittle, nondegradable silica glass based optical materials. In this review, we present an overview of the advances in polymeric optical material development, optical device design and fabrication techniques, and the accompanying applications to imaging, sensing and phototherapy.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.004

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 head0.050
GPT teacher head0.331
Teacher spread0.281 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations91
Published2018
Admission routes1
Has abstractyes

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