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Record W3175779577 · doi:10.1016/j.jobab.2021.06.001

Green CoNi2S4/porphyrin decorated carbon-based nanocomposites for genetic materials detection

2021· article· en· W3175779577 on OpenAlexvenueno aff
Mohammad Reza Saeb, Navid Rabiee, Farzad Seidi, Bahareh Farasati Far, Mojtaba Bagherzadeh, Éder C. Lima, Mohammad Rabiee

Bibliographic record

VenueJournal of Bioresources and Bioproducts · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsnot available
Fundersnot available
KeywordsPorphyrinBiosensorContext (archaeology)BiomoleculeNanocompositeCarbon fibersQuenching (fluorescence)FluorescenceChemistrySurface modificationNanotechnologyMaterials scienceCombinatorial chemistryPhotochemistryComposite number

Abstract

fetched live from OpenAlex

A one-pot synthesis method was conceptualized and implemented to develop green carbon-based nanocomposites working as biosensors. Porphyrin was synthesized to adorn the surface of nanocomposites making them highly sensitive for giving rise to π-π interactions between the genetic materials, proteins and porphyrin rings. The hydrogen bond formed between the proteins (analytes) and the nitrogen in the porphyrin structure as well as the surface hydroxyl groups was equally probable. In this context, different forms of porphyrins were incorporated to explore the interrelationship between the surface morphology and the ability of detection of genetic material and/or proteins by the aid of the synthesized structures. This phenomenon was conceptualized to optimize the interactions between the biomolecules and the substrate by reaching significant biosensor application in the presence of Anti-cas9 protein and sgRNA (concentration changed between 10 and 500 n mol/L). Almost full quenching of fluorescence emission was observed after addition of 300 n mol/L of Anti-cas9 protein and 250 n mol/L of sgRNA. Surprisingly, CoNi2S4 provided 12%–29% cytotoxicity in both HEK-293 and PC12 cell lines.

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 score0.012
Threshold uncertainty score0.493

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 head0.008
GPT teacher head0.236
Teacher spread0.228 · 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

Citations66
Published2021
Admission routes1
Has abstractyes

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