User-friendly and ultra-stable all-inclusive gold tablets for cysteamine detection
Bibliographic record
Abstract
system. The presence of cysteamine in the system introduced two types of inhibition which were dependent on the cysteamine concentration. By determining Michaelis-Menten's kinetic parameters, we gained mechanistic insights into the catalytic inhibition process. Based on the catalytic inhibition capability of cysteamine, the limit of detection (LoD) was calculated to be 69.04 and 82.9 μM in buffer and human serum samples, respectively. Finally, real human serum samples were tested, demonstrating the applicability of the pAuNP-Tablet for real-world applications. The % R values in human serum samples were in the range of 91-105% with % RSD less than 2% for all replicas. The stability tests over 16 months revealed the ultra-stable properties of the pAuNP-Tablet. Overall, with a simple fabrication method and a novel employed technique, this study contributes to the advancement of tablet-based sensors and helps in cysteamine detection in clinical settings.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".