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Record W1972673087 · doi:10.1021/la101701f

Biomineralization of Gold Nanoparticles by Lysozyme and Cytochrome c and Their Applications in Protein Film Formation

2010· article· en· W1972673087 on OpenAlexaff
Mandeep Singh Bakshi, Harpreet Kaur, Tarlok S. Banipal, Narpinder Singh, Gurinder Kaur

Bibliographic record

VenueLangmuir · 2010
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsCollege of the North AtlanticMount Saint Vincent University
Fundersnot available
KeywordsBiomineralizationLysozymeColloidal goldNanoparticleChemistryCytochrome cNanotechnologyProtein crystallizationChemical engineeringCrystallographyMaterials scienceOrganic chemistryBiochemistryCrystallizationMitochondrion

Abstract

fetched live from OpenAlex

Lysozyme (Lys) and cytochrome c (Cyc,c) proteins were used as mild reducing and stabilizing agents to synthesize gold nanoparticles (NPs) at precisely 40 and 80 degrees C. All reactions were monitored simultaneously by UV-visible measurements to determine changes in the nature of the protein during the course of reaction. The synthesis of Au NPs caused the simultaneous denaturation of protein due to the formation of bioconjugate NPs, and the denaturation temperature decreased with the number of NPs. Lys entrapped NPs in a typical gel state, and Cyc,c carried them on well-defined micelles at 80 degrees C or in the form of long fibrils or strands at 40 degrees C. The shape, size, and arrangement of bioconjugate NPs were characterized by atomic force microscopy and transmission electron microscopy measurements. Purified bioconjugate NPs were further used in zein protein film formation. The resulting films were characterized by photophysical and mechanical measurements. The induction of bioconjugate NPs made protein films isotropic and relatively more brittle (with a greater effect for Cyc,c than for Lys conjugate NPs) than in their absence and was considered to be well suited for biomedical applications.

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 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 score0.020
Threshold uncertainty score0.227

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 head0.004
GPT teacher head0.179
Teacher spread0.175 · 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 teacher head, 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

Citations56
Published2010
Admission routes1
Has abstractyes

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