Electrochemically modulated SERS detection of procaine using FTO electrodes modified with silver-decorated carbon nanosphere

Haroon, Muhammad; Abdulazeez, Ismail; Saleh, Tawfik A.; Al‐Saadi, Abdulaziz A.

doi:10.1016/j.electacta.2021.138463

Cited by 37 publications

(8 citation statements)

References 58 publications

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“…AMS 5 has a smaller charge transfer resistance and a higher current density, indicating that doping Ag is conducive to charge transfer between substrates and molecules. 20,[37][38][39][40][41] Based on the above results, the enhancement mechanism of AMS 5 is analyzed in Fig. 5e, showing certain feasible CT processes: (1) the electrons in AMS 5 are excited from the VB to CB; (2) then, electron excitation from the CB of AMS 5 transfers to the HUMO level; (3) similarly, electron excitation in R6G from the HOMO to LUMO; (4) the excited electron transfer from the R6G (LOMO) to the CB of AMS 5 .…”

Section: Dalton Transactions Papermentioning

confidence: 99%

Toward low-cost and sustainable SERS substrate: novel ultrasensitive AMS₅ nanoflowers

Xiong

Wei

et al. 2023

Dalton Trans.

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Section: Dalton Transactions Papermentioning

confidence: 99%

Toward low-cost and sustainable SERS substrate: novel ultrasensitive AMS₅ nanoflowers

Xiong

Wei

et al. 2023

Dalton Trans.

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“…Haroon et al reported a highly modified electrode SERS substrate which was a fluorine‐doped tin oxide electrode modified with silver carbon nanospheres for the detection of procaine (Haroon et al, 2021). This work demonstrated an in situ EC‐SERS approach, where the applied electrical potential for preconcentration was used to facilitate the interaction of the analyte and substrate over a time of 400 s. A very low detection limit of 10 −13 M (<1 ng/L) was reported using a benchtop 633 nm Raman instrument (Haroon et al, 2021).…”

Section: Seized Drug Analysismentioning

confidence: 99%

Transitioning surface‐enhanced Raman spectroscopy (SERS) into the forensic drug chemistry and toxicology laboratory: Current and future perspectives

Ott

Arroyo

2023

WIREs Forensic Science

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Applications and advances in surface-enhanced Raman spectroscopy (SERS) have grown over the last several years, demonstrating improvements in sensitivity and selectivity. Applications for SERS-related techniques have been explored in many analytical disciplines including forensic applications. Drug chemistry and toxicology may benefit from the growth of SERS since many methods have demonstrated high sensitivity, potential for quantitative analysis, and portable instrumentation for onsite testing. SERS techniques have been developed using many substrates including nanoparticles and colloids; microfluidic devices; paper-based substrates; electrodes; and wearable/flexible devices. These methods take advantage of the SERS phenomenon, providing more potential applications for Raman analysis and overcoming the traditional challenges of the technique. This is desirable for forensic drug chemistry applications which require screening and confirmatory approaches and analysis of mixtures with low weight percent contributions of analytes, as well as forensic toxicology, where matrix interferences, sensitivity requirements, and a growing introduction of novel drug entities pose challenges. Several chemometric approaches have been applied to Raman data to improve identification, classification, and quantification. These advances position SERS for incorporation into forensic chemistry laboratories. While some SERS applications are available commercially, widespread use of SERS in practicing forensic laboratories remains limited. Due to the requirements for use of analytical techniques in the courtroom, future studies for SERS should revolve around the assessment of validation parameters common in other analytical methods. SERS techniques that are rapid, simple, and inexpensive may be more quickly adapted for forensic testing, where use as advanced screening techniques is a clear avenue of research.

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“…However, surface-enhanced Raman scattering (SERS) provides a means of enhancing these signals, improving the sensitivity, and applications of Raman analysis. Electrochemical methods have been used to generate nanoparticles onto surfaces for the enhancement of Raman signals (reports of up to 10 10 enhancement factor) as a means to improve the detectability of low-concentration substances such as fentanyl [7][8][9][10][11][12]. More recently, Ott et al [13] presented an EC-SERS approach that enhanced the detectability of fentanyl and its derivatives with 87.5% accuracy.…”

Section: Introductionmentioning

confidence: 99%

Evaluation and classification of fentanyl‐related compounds using EC‐SERS and machine learning

Cooman

Ott

Arroyo

2023

Journal of Forensic Sciences

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Multiple analytical techniques for the screening of fentanyl‐related compounds exist. High discriminatory methods such as GC–MS and LC–MS are expensive, time‐consuming, and less amenable to onsite analysis. Raman spectroscopy provides a rapid, inexpensive alternative. Raman variants such as electrochemical surface‐enhanced Raman scattering (EC‐SERS) can provide signal enhancements with 1010 magnitudes, allowing for the detection of low‐concentration analytes, otherwise undetected using conventional Raman. Library search algorithms embedded in instruments utilizing SERS may suffer from accuracy when multicomponent mixtures involving fentanyl derivatives are analyzed. The complexing of machine learning techniques to Raman spectra demonstrates an improvement in the discrimination of drugs even when present in multicomponent mixtures of various ratios. Additionally, these algorithms are capable of identifying spectral features difficult to detect by manual comparisons. Therefore, the goal of this study was to evaluate fentanyl‐related compounds and other drugs of abuse using EC‐SERS and to process the acquired data using machine learning—convolutional neural networks (CNN). The CNN was created using Keras v 2.4.0 with Tensorflow v 2.9.1 backend. In‐house binary mixtures and authentic adjudicated case samples were used to evaluate the created machine‐learning models. The overall accuracy of the model was 98.4 ± 0.1% after 10‐fold cross‐validation. The correct identification for the in‐house binary mixtures was 92%, while the authentic case samples were 85%. The high accuracies achieved in this study demonstrate the advantage of using machine learning to process spectral data when screening seized drug materials comprised of multiple components.

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Electrochemically modulated SERS detection of procaine using FTO electrodes modified with silver-decorated carbon nanosphere

Cited by 37 publications

References 58 publications

Toward low-cost and sustainable SERS substrate: novel ultrasensitive AMS₅ nanoflowers

Toward low-cost and sustainable SERS substrate: novel ultrasensitive AMS₅ nanoflowers

Transitioning surface‐enhanced Raman spectroscopy (SERS) into the forensic drug chemistry and toxicology laboratory: Current and future perspectives

Evaluation and classification of fentanyl‐related compounds using EC‐SERS and machine learning

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Electrochemically modulated SERS detection of procaine using FTO electrodes modified with silver-decorated carbon nanosphere

Cited by 37 publications

References 58 publications

Toward low-cost and sustainable SERS substrate: novel ultrasensitive AMS5 nanoflowers

Toward low-cost and sustainable SERS substrate: novel ultrasensitive AMS5 nanoflowers

Transitioning surface‐enhanced Raman spectroscopy (SERS) into the forensic drug chemistry and toxicology laboratory: Current and future perspectives

Evaluation and classification of fentanyl‐related compounds using EC‐SERS and machine learning

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Product

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Toward low-cost and sustainable SERS substrate: novel ultrasensitive AMS₅ nanoflowers

Toward low-cost and sustainable SERS substrate: novel ultrasensitive AMS₅ nanoflowers