Development of Materials Strategies for Improving the Performance of Electrochemical and Photoelectrochemical Biosensors
Bibliographic record
Abstract
The shifting landscape of global healthcare emphasizes the need for rapid biomolecular detection at the point of care. Electrochemical signal transduction has excellently met this demand, delivering biosensors characterized by high sensitivity and low detection limits. The latest version of these biosensors provides point-of-care diagnosis through wearable devices. Additionally, there is growing promise in photoelectrochemical biosensing. These systems integrate optical excitation to enhance electrochemical signal readout and offers enhanced sensitivity by decoupling signal input and output. For effective application of photoelectrochemical technology in point-of-care diagnostics improving photoelectrode stability, lowering detection limits, and enhancing signal transduction efficiency are crucial. To address these challenges, we first developed a photoactive material system by integrating TiO2 as the inorganic semiconductive nanomaterial and modifying it with an organic catecholate molecule, pyrocatechol violet, along with graphene quantum dots to make photoelectrodes with enhanced baseline photocurrent generation and heightened photo-absorption in the visible range. The resulting photoactive material system demonstrated enhanced colloidal stability and improved biofunctionalization capabilities. Then, leveraging the high binding affinity of catecholates on TiO2 incorporating additional functional groups for enhanced biofunctionalization, we designed a signal-on photoelectrochemical materials system by biofunctionalizing of the photoelectrode with aptamers as the bioreceptor to create a universal bacterial biosensor. This signal-on aptamer-based assay detected Escherichia coli in urine at a limit-of-detection of 1913 CFU/mL, meeting the acceptable thresholds for identifying urinary tract infections and urosepsis. Finally, our focus shifted to develop a novel materials system for continuous and in vivo wearable biosensing, utilizing a microneedle-based system integrated with an electrochemical sensor for real-time target analysis in interstitial fluid. Given the suitability of electrochemical readout for continuous in vivo biosensing, we chose it over the photoelectrochemical transduction method. However, this system still leveraged the flexibility and structure switching capabilities of aptamers used in photoelectrochemical sensing. The developed wearable biosensor combines ultrasensitive aptamer-based electrochemical measurements for in situ biomarker analysis with hydrogel microneedles. Our wearable device offers strong mechanical properties for efficient skin penetration. It also exhibits high sensitivity and specificity in detecting clinically relevant concentrations of glucose and lactate in vivo, validated in two different healthy and unhealthy animal models. This highlights the potential of the wearable sensor in altering personalized diabetes management methods.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".