(Invited) A Versatile Fabrication Platform for Rapid Prototyping of Biosensors
Bibliographic record
Abstract
Background: Currently, translating biosensing platforms from the laboratory to the market is a lengthy process. One approach to expediting technology translation in this area is to develop rapid prototyping processes that enable an idea to be implemented into a sensor prototype rapidly and inexpensively. Furthermore, this process needs to be scalable to enable mid-volume and large-volume manufacturing for in-field testing and commercialization. In this work, we have developed a versatile rapid prototyping toolbox for creating tunable materials and integrating them into functional devices. Methods: biosensors are multi-lengthscale devices that are often optimized in the nm lengthscale for enhancing surface reaction kinetics and signal generation, in the micrometer lengthscale for addressing mass transport demands, and in the mm lengthscale for interfacing with the fluidic and electrical connections of the outside world. We have created a rapid prototyping toolbox based on craft cutting, self-assembly, electrochemical deposition, and wrinkling of thin films on shrink memory polymers to fabricate multi-lengthscale biosensors using tools available on the laboratory benchtop in a matter of hours. The benchtop processes developed here allow us to tune structural parameters such as roughness, aspect ratio, adhesion, porosity, and minimum feature sizes without the need for semiconductor cleanrooms. Results: We sought to create application-specific materials that translate structural tunability to functional tunability. For this purpose, we have studied the effect of electrode porosity, roughness, and surface area on the sensitivity of electrochemical biosensors. Through this study, we have found that by tuning the electrode morphology and porosity, we are able to enhance the observed electrocatalysis at electrode surfaces. These rapid surface reaction kinetics have enabled us to perform enzyme-free glucose detection in clinically-relevant concentrations of glucose. In addition to direct electrochemical sensing, we combined these newly-developed wrinkled materials with a DNA recognition layer to understand the role of electrode morphology on the sensitivity of DNA sensors. Through these experiments we find that significantly larger signal changes are observed when DNA is displayed on wrinkled surfaces with sub-10 nm pores. In order to create the optimal materials for surface-enhanced Raman spectroscopy (SERS), we used our newly developed rapid prototyping toolbox and combined layer-by-layer self-assembly of gold nanoparticles with substrate-mediated thin film wrinkling. This allowed us to precisely tune the nanoparticle size and separations from the sub-10nm to the sub-micrometer lengthscales. Due to the rapid prototyping nature of this method, we were able to develop application-specific SERS substrates without the need for numerical modeling. Conclusions: In conclusion, we have developed a rapid prototyping tools box that is designed to address the lengthscale engineering needs of the area of biosensing. Using this method, it is possible to rapidly screen through multiple structural parameters to manipulate biosensor sensitivity and signal transduction efficiency.
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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 | 0.000 | 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 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".