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Biomedical Application of Carbon Nanotubes for Proteins Extraction and Seperation

2016· article· en· W2559061339 on OpenAlexvenueno aff
Hartmut Schlüter, Mohammadreza Saboktakin

Bibliographic record

VenueJournal of Pharmacy and Nutrition Sciences · 2016
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsnot available
Fundersnot available
KeywordsCarbon nanotubeNanotechnologyMaterials science

Abstract

fetched live from OpenAlex

Measurement science and technology continue to play vital roles in biomedical research and in routine healthcare. Over recent decades there has been a steady evolution of sensors for biomedical measurement aimed at clinical care in hospitals, fundamental biomedical research in the laboratory, or even self-care in the home. The measurements of interest are diverse, ranging from pressure, force, flow and displacement to electrical field/charge, magnetic flux, and molecular species, such as gases, ions, proteins, bacteria, viruses, and DNA. In this review, we have studied several biomedical applications of nanotubes and nanowires for proteins measurements in cells. Also, These materials have a wide application as protein carriers and transporters. The wide applications of multi-walled carbon nanotubes (MWCNT) on the serious concerns about their safety on human health and environment have been studied.

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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.027
GPT teacher head0.347
Teacher spread0.320 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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Same venueJournal of Pharmacy and Nutrition SciencesSame topicCarbon Nanotubes in CompositesFrench-language works237,207