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Record W4405320264 · doi:10.3390/chemosensors12120259

Silsesquioxanes as Promising Materials for the Development of Electrochemical (Bio)Sensors

2024· article· en· W4405320264 on OpenAlexafffund
Felipe Zahrebelnei, Ariane Caroline Ribicki, Aline Martins Duboc Natal, Sérgio Toshio Fujiwara, Karen Wohnrath, Dhésmon Lima, Christiana Andrade Pessôa

Bibliographic record

VenueChemosensors · 2024
Typearticle
Languageen
FieldMaterials Science
TopicSilicone and Siloxane Chemistry
Canadian institutionsMount Saint Vincent University
FundersUniversidade Estadual de Ponta GrossaMount Saint Vincent University
KeywordsNanotechnologyElectrochemistryMaterials scienceChemistryElectrode

Abstract

fetched live from OpenAlex

Silsesquioxanes (SSQs) comprise an interesting and versatile class of three-dimensional organosilicate oligomers with diverse structural arrangements and interesting physicochemical properties. SSQs are of considerable technological interest, with applications that include the development of electrochemical detection devices. The presence of functional groups on their structures enables the anchoring of different electroactive and conductive species, such as complexes, metal nanoparticles and carbon nanomaterials, and biomolecules, including enzymes, nucleic acids, and antibodies, which boosts the sensitivity and selectivity of the obtained (bio)sensors. These materials can also be incorporated into conductive matrices using a range of methods, which enhances their versatility. This mini review provides an overview of the most recent applications of hybrid organic–inorganic SSQs in the preparation of modified electrodes for the development of electrochemical sensors and biosensors. Special focus is placed on the incorporation of nanomaterials in their polymeric structure and on the design and fabrication of electrochemical devices using different strategies.

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)0.000
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 score0.006
Threshold uncertainty score0.612

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.020
GPT teacher head0.275
Teacher spread0.256 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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