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Record W3004561509 · doi:10.1155/2020/5659682

Conducting Polymer-Based Composite Materials for Therapeutic Implantations: From Advanced Drug Delivery System to Minimally Invasive Electronics

2020· article· en· W3004561509 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Polymer Science · 2020
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsUniversity of Guelph
FundersGuangdong Science and Technology DepartmentChina Postdoctoral Science FoundationSun Yat-sen UniversityNational Natural Science Foundation of China
KeywordsInterfacingMaterials scienceElectronicsDrug deliveryNanotechnologyPolymerComposite materialComputer scienceEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Conducting polymer-based composites have recently becoming popular in both academic research and industrial practices due to their high conductivity, ease of process, and tunable electrical properties. The multifunctional conducting polymer-based composites demonstrated great application potential for in vivo therapeutics and implantable electronics, including drug delivery, neural interfacing, and minimally invasive electronics. In this review article, the state-of-the-art conducting polymer-based composites in the mentioned biological fields are discussed and summarized. The recent progress on the synthesis, structure, properties, and application of the conducting polymer-based composites is presented, aimed at revealing the structure-property relationship and the corresponding functional applications of the conducting polymer-based composites. Furthermore, key issues and challenges regarding the implantation performance of these composites are highlighted in this paper.

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.

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 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 score0.007
Threshold uncertainty score0.617

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.038
GPT teacher head0.306
Teacher spread0.269 · 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