Optimization of gold nanorod arrays for surface enhanced Raman spectroscopy (SERS) detection of atrazine
Bibliographic record
Abstract
Recently, there has been increasing concern over the widespread use of the herbicide atrazine which has been reported to have problematic side effects on local ecosystems. This has highlighted the need for rapid and accurate point-of-need assessment tools for analytical determination of herbicides in ground and surface waters. Surface enhanced Raman spectroscopy (SERS) is a sensitive vibrational spectroscopy technique which has recently been employed for the analysis of a variety of analytes in water, ranging from pharmaceuticals to pesticides. In this work, SERS sensors constructed using gold nanorod (AuNR) arrays are optimized and then utilized for the rapid and sensitive detection of atrazine. In this study, the effect of relative humidity on the self-assembly of gold nanorods into arrays was explored, and the SERS performance was assessed using para-aminothiophenol as a SERS probe. Once the SERS performance of the substrates was deemed optimal, the detection of atrazine was highlighted. This work represents the first time that relative humidity has been explored as an optimization strategy for controlled alignment of gold nanorods for SERS analysis of atrazine.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".