scholarly journals Erratum: “Visible wavelength surface-enhanced Raman spectroscopy from In-InP nanopillars for biomolecule detection” [Appl. Phys. Lett. 109, 253105 (2016)]

2017 ◽  
Vol 110 (13) ◽  
pp. 139901
Author(s):  
B. J. Murdoch ◽  
J. F. Portoles ◽  
S. Tardio ◽  
A. J. Barlow ◽  
I. W. Fletcher ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Surface Enhanced Raman Spectroscopy ◽  
Biomolecule Detection ◽  
Visible Wavelength ◽  
Surface Enhanced ◽  
Surface Enhanced Raman

scholarly journals Visible wavelength surface-enhanced Raman spectroscopy from In-InP nanopillars for biomolecule detection

2016 ◽  
Vol 109 (25) ◽  
pp. 253105 ◽  
Author(s):  
B. J. Murdoch ◽  
J. F. Portoles ◽  
S. Tardio ◽  
A. J. Barlow ◽  
I. W. Fletcher ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Surface Enhanced Raman Spectroscopy ◽  
Biomolecule Detection ◽  
Visible Wavelength ◽  
Surface Enhanced ◽  
Surface Enhanced Raman

Ordered Ag/Si Nanowires Array: Wide-Range Surface-Enhanced Raman Spectroscopy for Reproducible Biomolecule Detection

Nano Letters ◽  
2013 ◽  
Vol 13 (11) ◽  
pp. 5039-5045 ◽  
Author(s):  
Jian-An Huang ◽  
Ying-Qi Zhao ◽  
Xue-Jin Zhang ◽  
Li-Fang He ◽  
Tai-Lun Wong ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Surface Enhanced Raman Spectroscopy ◽  
Si Nanowires ◽  
Biomolecule Detection ◽  
Wide Range ◽  
Surface Enhanced ◽  
Surface Enhanced Raman ◽  
Nanowires Array

scholarly journals SERSNet: Surface-Enhanced Raman Spectroscopy Based Biomolecule Detection Using Deep Neural Network

Biosensors ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 490
Author(s):  
Seongyong Park ◽  
Jaeseok Lee ◽  
Shujaat Khan ◽  
Abdul Wahab ◽  
Minseok Kim
Keyword(s):  
Machine Learning ◽  
Raman Spectroscopy ◽  
Test Performance ◽  
Model Development ◽  
Surface Enhanced Raman Spectroscopy ◽  
Learning Models ◽  
Biomolecule Detection ◽  
Surface Enhanced ◽  
Surface Enhanced Raman ◽  
Machine Learning Models

Surface-Enhanced Raman Spectroscopy (SERS)-based biomolecule detection has been a challenge due to large variations in signal intensity, spectral profile, and nonlinearity. Recent advances in machine learning offer great opportunities to address these issues. However, well-documented procedures for model development and evaluation, as well as benchmark datasets, are lacking. Towards this end, we provide the SERS spectral benchmark dataset of Rhodamine 6G (R6G) for a molecule detection task and evaluate the classification performance of several machine learning models. We also perform a comparative study to find the best combination between the preprocessing methods and the machine learning models. Our best model, coined as the SERSNet, robustly identifies R6G molecule with excellent independent test performance. In particular, SERSNet shows 95.9% balanced accuracy for the cross-batch testing task.

Bacterial detection and differentiation via direct volatile organic compound sensing with surface enhanced Raman spectroscopy

2017 ◽  
Author(s):  
Caitlin S. DeJong ◽  
David I. Wang ◽  
Aleksandr Polyakov ◽  
Anita Rogacs ◽  
Steven J. Simske ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Bacterial Infections ◽  
Direct Detection ◽  
Point Of Care ◽  
Surface Enhanced Raman Spectroscopy ◽  
E Coli ◽  
Recent Emergence ◽  
Surface Enhanced ◽  
Volatile Organic ◽  
Surface Enhanced Raman

Through the direct detection of bacterial volatile organic compounds (VOCs), via surface enhanced Raman spectroscopy (SERS), we report here a reconfigurable assay for the identification and monitoring of bacteria. We demonstrate differentiation between highly clinically relevant organisms: <i>Escherichia coli</i>, <i>Enterobacter cloacae</i>, and <i>Serratia marcescens</i>. This is the first differentiation of bacteria via SERS of bacterial VOC signatures. The assay also detected as few as 10 CFU/ml of <i>E. coli</i> in under 12 hrs, and detected <i>E. coli</i> from whole human blood and human urine in 16 hrs at clinically relevant concentrations of 10<sup>3</sup> CFU/ml and 10<sup>4</sup> CFU/ml, respectively. In addition, the recent emergence of portable Raman spectrometers uniquely allows SERS to bring VOC detection to point-of-care settings for diagnosing bacterial infections.

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