Silsesquioxanes as Promising Materials for the Development of Electrochemical (Bio)Sensors
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".