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Non-functionalized carbon nanotube binding with hemoglobin

2008· article· en· W1971040947 on OpenAlex
X C Wu, Wenjun Zhang, Ramaswami Sammynaiken, Qing H. Meng, Q. Yang, En Zhan, Qinying Liu, Wei Yang, Rui Wang

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Physics Conference Series · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHemoglobin structure and function
Canadian institutionsLakehead UniversityUniversity of TorontoUniversity of Saskatchewan
FundersCanadian Institutes of Health Research
KeywordsCarbon nanotubeBiosensorNanotubeRaman spectroscopyHemoglobinChemistryNanotechnologyExcitation wavelengthLuminescenceMaterials scienceOrganic chemistryWavelengthOptoelectronics

Abstract

fetched live from OpenAlex

Carbon nanotube has a high potential to be used as a biosensor and drug carrier. However, its binding behaviour with proteins needs to be studied before the full potential of carbon nanotube in biological studies can be realized. Although many studies have been conducted to characterize the affinity of functionalized carbon nanotube to various types of proteins, our present study for the first time reported that hemoglobin can bind with non-functionalized carbon nanotube, and this binding can be identified by Raman spectrum. Further, this binding has not change, Raman luminescence with specific excitation and emission wavelengths. The immediate application of these findings is to use non-functionalized carbon nanotube as a biosensor to measure H2S in blood in which hemoglobin takes about 37% of the total blood volume. Other potential uses of non-functionalized carbon nanotube to bind selective groups of proteins are also hinted.

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)0.000
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 score0.011
Threshold uncertainty score0.443

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.013
GPT teacher head0.216
Teacher spread0.204 · 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