Considerations on Circuit Design and Data Acquisition of a Portable Surface Plasmon Resonance Biosensing System
Bibliographic record
Abstract
The aim of this study was to develop a circuit for an inexpensive portable biosensing system based on surface plasmon resonance spectroscopy. This portable biosensing system designed for field use is characterized by a special structure which consists of a microfluidic cell incorporating a right angle prism functionalized with a biomolecular identification membrane, a laser line generator and a data acquisition circuit board. The data structure, data memory capacity and a line charge-coupled device (CCD) array with a driving circuit for collecting the photoelectric signals are intensively focused on and the high performance analog-to-digital (A/D) converter is comprehensively evaluated. The interface circuit and the photoelectric signal amplifier circuit are first studied to obtain the weak signals from the line CCD array in this experiment. Quantitative measurements for validating the sensitivity of the biosensing system were implemented using ethanol solutions of various concentrations indicated by volume fractions of 5%, 8%, 15%, 20%, 25%, and 30%, respectively, without a biomembrane immobilized on the surface of the SPR sensor. The experiments demonstrated that it is possible to detect a change in the refractive index of an ethanol solution with a sensitivity of 4.99838 × 10(5) ΔRU/RI in terms of the changes in delta response unit with refractive index using this SPR biosensing system, whereby the theoretical limit of detection of 3.3537 × 10(-5) refractive index unit (RIU) and a high linearity at the correlation coefficient of 0.98065. The results obtained from a series of tests confirmed the practicality of this cost-effective portable SPR biosensing system.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.003 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".