Can Electrochemical Aptasensors Achieve the Commercial Success of Glucose Biosensors?
Bibliographic record
Abstract
Abstract Enzymes and antibodies are widely available biorecognition elements in bioanalytical tools such as personal glucose monitoring (PGM) devices and lateral flow assays (LFA). Meanwhile, electrochemical aptamer‐based (EAB) sensors are promising affinity‐based bioanalytical tools with potential advantages over such conventional bioassays. However, several critical factors affect the stability of EAB sensors, pivotal for their commercialization including 1) electrode defects due to surface treatment methods, 2) hampering effects of redox molecules, 3) electrical potential‐induced aptamer detachment, 4) thermal‐induced monolayer solubilization, 5) biochemical/enzymatic degradation, 6) biofouling, and 7) inadequate statistical design and analysis in EAB sensor fabrication. Herein, antidotes for the obstacles are proposed by applying novel surface treatment methods, adapting redox molecule, tuning electrochemical tests, tweaking backfilling agents, and anti‐bio‐fouling coatings. Nonetheless, the obstacles are a driving force to clear pathways toward bringing EAB sensors to the market for therapeutic drug and metabolite monitoring, point of care sensors, macromolecule detection, and pathogen diagnostics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".