An Affordable Microsphere-Based Device for Visual Assessment of Water Quality
Bibliographic record
Abstract
This work developed a prototype of an affordable, long-term water quality detection device that provides a visual readout upon detecting bacterial contamination. This device prototype consists of: (1) enzyme-releasing microspheres that lyse bacteria present in a sample, (2) microspheres that release probes that bind the DNA of the lysed bacteria, and (3) a detector region consisting of gold nanoparticles. The probes bind bacterial DNA, forming complexes. These complexes induce aggregation of the gold nanoparticles located in the detector region. The nanoparticle aggregation process causes a red to blue color change, providing a visual indicator of contamination being detected. Our group fabricated and characterized microspheres made of poly (ε-caprolactone) that released lysozyme (an enzyme that degrades bacterial cell walls) and hairpin DNA probes that bind to regions of the Escherichia coli genome over a 28-day time course. The released lysozyme retained its ability to lyse bacteria. We then showed that combining these components with gold nanoparticles followed by exposure to an E. coli-contaminated water sample (concentrations tested—106 and 108 cells/mL) resulted in a dramatic red to blue color change. Overall, this device represents a novel low-cost system for long term detection of bacteria in a water supply and other applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".