A Smartphone Operated Electrochemical Reader and Actuator that Streamlines the Operation of Electrochemical Biosensors
Bibliographic record
Abstract
Prompted by the increasing number of electrochemical biosensors reported in the literature, a wide range of lab-made potentiostats have been developed by researchers in recent years. While these devices are less costly than their commercial counterparts, they are typically single-plex and rely on non-integrated sample preparation or signal actuation devices. To address these limitations, we have designed a portable and fully integrated platform for point-of-care (PoC) electrochemical readout and actuation. This device performs standard voltammetric techniques and is controlled remotely by an accompanying smartphone application via Bluetooth Low Energy (BLE). This device supports both standard three-electrode and dual signal assays and can be extended to support multiple channels. Our device also integrates a portable heater and an electromagnet to facilitate away from lab sample heating and magnetic manipulation respectively. This device was used to detect nucleic acids and bacterial targets using single-stranded DNA probes and redox DNAzymes, respectively. The small form-factor and low cost of this device, in conjunction with the integration of peripheral instruments and native multiplex analysis capabilities, will enable electrochemical biosensing to be performed outside the research laboratory.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".