Low-cost Bench-Top Microfabrication of Nano/Microstructured Electrodes for Electrochemical Biosensing
Bibliographic record
Abstract
The lack of safe drinking water, access to medical treatment and equipment, and sus-tainable energy are some examples of problems affecting the majority of developing nations today, and are collectively responsible for over 15 million annual deaths globally. While tech-nological advances have enabled developed countries to improve the average health quality and overall life expectancy of their populations, the adoption of such technologies is cost-prohibitive for countries with small healthcare budgets. One of the obstacles in achieving low-cost and simple-to-use biotechnologies are versatile and robust fabrication methods. Therefore, there is great demand for novel and feasible biomedical device technologies that can address the current healthcare challenges of resource-limited nations. In this thesis, a low-cost and rapid bench-top fabrication method is introduced to cre-ate nano/microstructured electrodes (NMSEs) with applications in microfluidic cell sensing, enhanced energy capture, and hemolytic agent detection. Metal deposition and viscoelastic shape-memory polymers were used to rapidly create highly tuneable wrinkled electrodes with electrochemical surface area enhancements of up to 650% and miniaturization down to 16% from the original area. These shrunken metallic electrodes were transferred onto polydime-thylsiloxane (PDMS) using a dissolvable photoresist liftoff technique. The result was a new, all PDMS-based flexible microfluidic cell sensor, capable of detecting 3T3 fibroblast cells down to 2x106 cells/ml and able to withstand flowrates of up to 100 mL/min. In a second project, biofilms of Geobacter sulfurreducens were cultured onto wrinkled NMSEs with enhanced electro-active surface area, which served as bioanodes in a microbial fuel cell. This enhanced microbial fuel cell generated twice the power output of control devices containing planar electrodes in lieu of the larger wrinkled variety. Next, we developed a phospholipid membrane-on-a-chip platform for the electrochemical detection of membrane disrupting agents. We deposited a coating of 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) phospholipids on the sur-face of the NMSEs to inhibit charge transfer between the redox reporter molecule in solution and the electrode. Lytic compounds were hypothesized to disrupt the coating, such that the exposed NMSE interface could facilitate the monitoring of the signal transduction from redox Ph.D. Thesis – Sokunthearath Saem McMaster – Chemistry and Chemical Biology iv molecules in solution. A first round of experiments tested the DMPC-coated NMSEs against commercially available sodium dodecyl sulfate (SDS) and Polymyxin-B (PmB), an antibiotic known to cause membrane rupture through dissolution and pore formation. The results indi-cated viable devices with limits of detection at 10 ppm and 1 ppm for SDS and PmB respec-tively. Lastly, we added cholesterol to the DMPC phospholipids to create stable supported membranes on NMSEs. The addition of cholesterol to DMPC increased the supported mem-brane stability against SDS and PmB where pure DMPC membranes produce higher signal recovery than cholesterol rich membranes. As a proof-of-concept, we tested the cholesterol rich membranes against Pneumolysin (PLY), a hemolytic protein known to rupture cell mem-branes. The LOD of SDS, PmB, and PLY was determined to be 500 ppm, 1 ppm, and 600 ppb respectively. All three electroanalytical devices produced using our shape-memory poly-mer structuring technique exemplify the potential of this versatile platform to help address the issues currently facing developing countries.
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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) | 0.001 | 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".