MétaCan
Menu
Back to cohort
Record W3204947374 · doi:10.1088/2043-6262/ac2745

Tuning the nanostructural properties of silver nanoparticles for optimised surface enhanced Raman scattering sensing of SARS CoV-2 spike protein

2021· article· en· W3204947374 on OpenAlexaff
Kais Daoudi, Krithikadevi Ramachandran, Soumya Columbus, Abdelaziz Tlili, Mona Mahfood, My Alı El Khakani, Mounir Gaidi

Bibliographic record

VenueAdvances in Natural Sciences Nanoscience and Nanotechnology · 2021
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsSpike (software development)Materials scienceRaman scatteringSpike ProteinRaman spectroscopyNanoparticleSurface (topology)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Silver nanoparticleNanotechnologyCoronavirus disease 2019 (COVID-19)OpticsComputer sciencePhysicsMedicine

Abstract

fetched live from OpenAlex

Abstract In this work, development of fast, selective and highly sensitive sensor for detecting severe acute respiratory syndrome coronavirus 2 (known as SARS CoV-2 or COVID19) spike protein has been reported. Surface enhanced Raman spectroscopy (SERS) based direct detection of spike protein was investigated by fabricating silver nanoparticles decorated Si (AgNPs/Si) using a facile wet chemical process. Fabrication parameters such as immersion time and precursor concentration were varied; their corresponding morphological characteristics were well elucidated while assuring homogeneous decoration of AgNPs on Si substrate. Classical dye molecule such as rhodamine 6G (R6G) was utilised for the optimisation of fabricated sensor. Based on acquired intensity of R6G, the best sensor was selected, which was further employed for label free direction of spike protein. The developed sensor exhibited high sensitivity in pico-molar range with excellent reproducibility. Selectivity studies were demonstrated using DNA and H1N1 protein as well. The current findings will launch new state of art in the field of medical diagnostics.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.019
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.004
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 head0.032
GPT teacher head0.333
Teacher spread0.300 · 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.

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

Citations19
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueAdvances in Natural Sciences Nanoscience and NanotechnologySame topicSARS-CoV-2 and COVID-19 ResearchFrench-language works237,207