Recent developments in optofluidic-assisted Raman spectroscopy

Mak, Jason C. C.; Rutledge, Steve; Abu-Ghazalah, Rashid M.; Eftekhari, F.; Irizar, J.; Tam, Natalie C. M.; Zheng, Gang; Helmy, Amr S.

doi:10.1016/j.pquantelec.2012.11.001

Cited by 29 publications

(29 citation statements)

References 160 publications

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“…A number of excellent review papers on the advancements of optofluidics exist in the literature. These reviews focused on specific applications, such as optofluidic sensors [19,20], spectrometers [21], cytometers [22], and tweezers [23,24], etc. or specific components, such as waveguides [25,26] and lenses [27,28].…”

Section: Introductionmentioning

confidence: 99%

Toward the commercialization of optofluidics

Song

Nguyen

Tan

2017

Microfluid Nanofluid

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Optofluidics is a marriage between the field of optics and microfluidics. This field aims at providing practical solutions with the integration of optical tools into lab-on-chip systems. Often, this results in opportunities for commercialization due to the advancement offered after the integration. Although numerous novel functions and properties have been demonstrated with the combination of optics and microfluidics, the market has witnessed only few transferals of optofluidic technologies from academic laboratories. This stemmed from a lack of a "killer applications" despite several decades of development. Therefore it is necessary to have a retrospective review on this topic, particularly on the basic optofluidic components, to analyze what might be the hurdles to stop the market uptake of optofluidic devices. Specifically, this review paper is focused on discussion of optofluidic components in terms of fabrication standardization, device & operational cost and practicability for end-users. It is believed that these factors play important roles in the market uptake of a novel technology. We then provide perspectives on how to align the development of optofluidics with the requirements imposed by the industry.

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Section: Introductionmentioning

confidence: 99%

Toward the commercialization of optofluidics

Song

Nguyen

Tan

2017

Microfluid Nanofluid

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“…The Raman spectra for the HAuCl 4 incubation conditions contained two very strong peaks at 1358 and 1571 cm −1 . The position peaks corresponding to the symmetric and asymmetric stretching carboxylic groups (COO −1 ) are related to the peaks observed at 1358 and 1571 cm −1 , respectively [38]. The observed vibrations have also been reported for free and Na feluric acid salt [39].…”

Section: Resultsmentioning

confidence: 74%

Facile Synthesis of Monodisperse Gold Nanocrystals Using Virola oleifera

et al. 2016

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The development of new routes and strategies for nanotechnology applications that only employ green synthesis has inspired investigators to devise natural systems. Among these systems, the synthesis of gold nanoparticles using plant extracts has been actively developed as an alternative, efficient, cost-effective, and environmentally safe method for producing nanoparticles, and this approach is also suitable for large-scale synthesis. This study reports reproducible and completely natural gold nanocrystals that were synthesized using Virola oleifera extract. V. oleifera resin is rich in epicatechin, ferulic acid, gallic acid, and flavonoids (i.e., quercetin and eriodictyol). These gold nanoparticles play three roles. First, these nanoparticles exhibit remarkable stability based on their zeta potential. Second, these nanoparticles are functionalized with flavonoids, and third, an efficient, economical, and environmentally friendly mechanism can be employed to produce green nanoparticles with organic compounds on the surface. Our model is capable of reducing the resin of V. oleifera, which creates stability and opens a new avenue for biological applications. This method does not require painstaking conditions or hazardous agents and is a rapid, efficient, and green approach for the fabrication of monodisperse gold nanoparticles. Graphical AbstractThe Virola oleifera reduction method for the synthesis of gold nanoparticles (AuNP’s)

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“…Details on liquid‐core waveguide enhancement of fluidic samples can be found in our previous research. ()…”

Section: Methodsmentioning

confidence: 99%

“…It provides excellent chemical specificity with minimal sample preparation, and can preserve the fluid under test for further analysis. () The challenge associated with Raman approaches is their weak signal, when compared to luminescence. Enhancement techniques such as surface enhanced Raman or Coherent anti‐stokes Raman require cumbersome chemical preparation with limited repeatability or expensive, complex instrumentation, respectively.…”

Section: Introductionmentioning

confidence: 99%

Optimized preprocessing and machine learning for quantitative Raman spectroscopy in biology

Storey

Helmy

2019

J Raman Spectroscopy

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Raman spectroscopy's capability to provide meaningful composition predictions is heavily reliant on a preprocessing step to remove insignificant spectral variation. This is crucial in biofluid analysis. Widespread adoption of diagnostics using Raman requires a robust model that can withstand routine spectra discrepancies due to unavoidable variations such as age, diet, and medical background. A wealth of preprocessing methods are available, and it is often up to trial-and-error or user experience to select the method that gives the best results. This process can be incredibly time consuming and inconsistent for multiple operators. In this study, we detail a method to analyze the statistical variability within a set of training spectra and determine suitability to form a robust model. This allows us to selectively qualify or exclude a preprocessing method, predetermine robustness, and simultaneously identify the number of components that will form the best predictive model. We demonstrate the ability of this technique to improve predictive models of two artificial biological fluids. Raman spectroscopy is ideal for noninvasive, nondestructive analysis. Routine health monitoring that maximizes comfort is increasingly crucial, particularly in epidemic-level diabetes diagnoses. High variability in spectra of biological samples can hinder Raman's adoption for these methods. Our technique allows the decision of optimal pretreatment method to be determined for the operator; model performance is no longer a function of user experience. We foresee this statistical technique being an instrumental element to widening the adoption of Raman as a monitoring tool in a field of biofluid analysis. KEYWORDS chemometrics, machine learning, preprocessing methods, quantiative biological analysis, Raman spectroscopy 958

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Recent developments in optofluidic-assisted Raman spectroscopy

Cited by 29 publications

References 160 publications

Toward the commercialization of optofluidics

Toward the commercialization of optofluidics

Facile Synthesis of Monodisperse Gold Nanocrystals Using Virola oleifera

Optimized preprocessing and machine learning for quantitative Raman spectroscopy in biology

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