Surface-Enhanced Raman Spectroscopy for the Detection of Reactive Oxygen Species
Bibliographic record
Abstract
Reactive oxygen species (ROS) play fundamental roles in various biological and chemical processes in nature and industries, including cell signaling, disease development and aging, immune defenses, environmental remediation, pharmaceutical syntheses, metal corrosion, energy production, etc. As such, their detection is of paramount importance, but their accurate identification and quantification are technically challenging due to their transient nature with short lifetimes and low steady-state concentrations. As a highly sensitive and selective analytical technique, surface-enhanced Raman spectroscopy (SERS) is promising for detecting ROS in real-time, enabling in situ monitoring of ROS-involved electrochemical and biochemical events with exceptional resolution. This review provides a comprehensive analysis of the state-of-the-art in the SERS-based detection of ROS. Herein, the principles and ROS sensing mechanisms of SERS have been critically evaluated, highlighting their emerging applications in direct and indirect ROS monitoring in electrochemical and biological systems. The developments and reaction schemes of selective SERS probes for superoxide ( • O 2 – ), hydroxyl radicals ( • OH), nitric oxide ( • NO), peroxynitrite (ONOO – ), and hypochlorite (OCl – ) are presented. Finally, technical challenges and future research directions are discussed to guide the design of SERS for ROS analysis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".