Polymer Brush–GaAs Interface and Its Use as an Antibody-Compatible Platform for Biosensing
Bibliographic record
Abstract
Abstract Despite evidence showing that polymer brushes (PBs) are a powerful tool used in biosensing for minimizing nonspecific interactions, allowing for optimization of biosensing performance, and the fact that GaAs semiconductors have proven to have a remarkable potential for sensitive biomolecule detection, the combination of these two robust components has never been considered nor evaluated as a platform for biosensing applications. This work reports different methodologies to prepare and tune PBs on the GaAs interface (PB–GaAs) and their potential as useful platforms for antibody grafting, with the ultimate goal of demonstrating the innovative and attractive character of the PB–GaAs interfaces in the enhanced capture of antibodies and control of nonspecific interactions. Three different functionalization approaches were explored, one “grafting-to” and two “grafting-from,” in which atom transfer radical polymerization (ATRP) was performed, followed by their corresponding characterizations. Demonstration of the compatibility of Escherichia coli (E. coli) and Legionella pneumophila (Lp) antibodies with the PB–GaAs platform compared to the results obtained with conventional biosensing architectures developed for GaAs indicates the attractive potential for operation of a sensitive biosensor. Furthermore, these results showed that by carefully choosing the nature and preparation methodology of a PB–GaAs interface, it is possible to effectively tune the affinity of PB–GaAs-based sensors toward E. coli and Lp antibodies ultimately demonstrating the superior specificity of the developed biosensing platform.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".