Nanoscale Approaches for Optical and Electrochemical Biomolecular Recognition
Bibliographic record
Abstract
The development of new methods for the direct detection of infectious diseases (i.e., bacterial and viral infections) using reagent-free assays is an essential but challenging problem. Current electrochemical or optical measurement systems have been beneficial in developing diagnostic tools in the last decade, but their use has been limited as they require multiple preparation steps that make them non-ideal for applications in medical devices. I dedicated my Ph.D. research to developing systems that work independently and do not require additional sample preparation and washing steps. My focus can be categorized into three different venues: 1: development of an optical method to visualize bacterial contamination on common surfaces. 2: a reagent-free detection method to directly analyze viral particles and proteins that serve as biomarkers of infectious disease 3: manipulating physical properties of electrodes and electrochemical sensing surfaces for proposing affordable sensing platforms.My Ph.D. thesis includes four main chapters: 1. The first chapter consists of a literature review exploring the fundamentals of electrochemical biosensing and the current applications of nanostructuring in the construction of sensitive biosensors for in vivo and in vitro measurement. 2. A project on developing spray-on optical sensors for in situ detection of bacterial contamination in healthcare facilities. This project includes synthesizing environmentally friendly nanoparticles, InP/ZnSe/ZnS quantum dots, and their conjugation to bacteria selective aptamers. iii The construct is used in combination with a hand-held imaging device to screen surfaces for bacteria contaminations to avoid breakouts and hospital-acquired infections. 3. This chapter expands on developing a reagentless sensing method to directly detect and differentiate viral particles in a reagent-less manner. This electrochemical assay uses saliva samples and detects if the subject is infected with the SARS-CoV-2 virus in less than five minutes. 4. The invention of a physically modified electrochemical surface for achieving ultra-sensitive detection of proteins and viruses on screen printed electrodes (SPE). Using SPE allows for eliminating the need for microfabrication in order to propose affordable alternatives for point-of-care testing problems. As a proof-of-concept, we have shown the detection of viral particles and proteins in SARS-CoV-2 infected patient samples.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".