Photoacoustic characterization of bovine serum albumin interaction with gold nanourchin in phosphate buffer saline and the stochastic behaviour
Bibliographic record
Abstract
Abstract The interaction of pulse Nd:YAG laser with bovine serum albumin (BSA) containing gold nanourchins (GNU) is studied using the photoacoustic (PA) technique. UV–Vis spectroscopy indicated the absorbance S1 > S2 due to higher adsorption of BSA when the GNU concentration was increased. S1 was found to be a weakly absorbing medium but with the addition of GNU (i.e. S2 and S3) became strongly absorbing. The maximum signal amplitude exhibited a linear increase up to ≈1.5 J cm −2 but beyond this was increased more rapidly suggesting that the absorption loss in the medium is non-linearly related to the fluence. The pulse width of the tensile component became narrower than the compression component hence producing higher pressure amplitudes, hence increased the corresponding PA signal in the order of S1 > S2 > S3. In the cases of S2 and S3, the laser-induced pressure wave decreased in amplitude with GNU concentration and the pulse width broadened. The oscillatory behaviour of the BSA–GNU interface due to the adsorption and desorption process was demonstrated by a CMOS sensor as spatial field intensity disturbance. PA scanning revealed a stochastic response with an asymmetric shape of waveforms. This technique shows a potential use for biomedical diagnostic applications provided the inherent stochastic nature of colloidal behaviour at a small scale either in static or scanning modes are considered.
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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 | 0.000 | 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) | 0.001 | 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 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".