Raman spectra of poly(2-pyridinium hydrochloride-2-pyridylacetylene)

Millen, Ricardo Prado; Temperini, Márcia L. A.; Faria, Dalva Lúcia Araújo de; Batchelder, D. N.

doi:10.1002/(sici)1097-4555(199911)30:11<1027::aid-jrs442>3.0.co;2-e

Cited by 11 publications

(8 citation statements)

References 21 publications

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“…By exciting the outermost layer of human skin and nail at 632.8 nm, De Faria and Souza [1] could photobleach initially strong fluorescent background and reject the residual emission by spatial filtering. Millen et al [2] recorded the Raman spectra of poly(2-pyridinium hydrochloride-2-pyridylacetylene) in the UV region (244 nm) to avoid fluorescence. In order to reduce the fluorescence intensity, Pelletier and Altkorn [3] collected Raman spectra in liquid-core optical fiber waveguides.…”

Section: Introductionmentioning

confidence: 99%

An intelligent background‐correction algorithm for highly fluorescent samples in Raman spectroscopy

Zhang

Chen

Liang

et al. 2009

J Raman Spectroscopy

271

225

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Fluorescent background is a major problem in recoding the Raman spectra of many samples, which swamps or obscures the Raman signals. The background should be suppressed in order to perform further qualitative or quantitative analysis of the spectra. For this purpose, an intelligent background-correction algorithm is developed, which simulates manual background-correction procedure intelligently. It basically consists of three aspects: (1) accurate peak position detection in the Raman spectrum by continuous wavelet transform (CWT) with the Mexican Hat wavelet as the mother wavelet; (2) peak-width estimation by signal-to-noise ratio (SNR) enhancing derivative calculation based on CWT but with the Haar wavelet as the mother wavelet; and (3) background fitting using penalized least squares with binary masks. This algorithm does not require any preprocessing step for transforming the spectrum into the wavelet space and can suppress the fluorescent background of Raman spectra intelligently and validly. The algorithm is implemented in R language and available as open source software (http://code.google.com/p/baselinewavelet).

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Section: Introductionmentioning

confidence: 99%

An intelligent background‐correction algorithm for highly fluorescent samples in Raman spectroscopy

Zhang

Chen

Liang

et al. 2009

J Raman Spectroscopy

271

225

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“…The problems associated with this fluorescence were first overcome by Batchelder et al 7 for water-soluble poly-(2-pyridinium hydrochloride-2-pyridylacetylene) (P2EPH). Two pathways towards overcoming this problem were used: (i) measurements of Raman spectra in the resonance region and (ii) utilization of surface-enhanced Raman spectroscopy.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, it was demonstrated that the enhancement mechanism is essentially electromagnetic and the vibrational modes are not strongly affected by the metal surface. 7 The vibrational assignment was also presented. 7,9 The frequencies of some vibrational modes assigned to polyacetylenic chain vibrations showed a dependence on the energy of the exciting radiation.…”

Section: Introductionmentioning

confidence: 99%

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Morphology and optical responses of SERS active π-conjugated poly(N-ethyl-2-ethynylpyridinium iodide)/Ag nanocomposite systems

Dammer

Vlčková

Procházka

et al. 2009

Phys. Chem. Chem. Phys.

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The influence of the poly(N-ethyl-2-ethynylpyridinium iodide) (PEEP-I) concentration on the morphology and optical properties of nanocomposite systems prepared by mixing the polymer solution with a hydrosol of ca. 9 nm Ag nanoparticles (NPs) was investigated by a combination of surface plasmon extinction (SPE) measurements, transmission electron microscope (TEM) imaging and surface-enhanced Raman spectroscopy (SERS). The PEEP-I concentration was found to have a strong impact on the assembly of Ag NPs and, consequently, on the optical responses of the composite systems. At low polymer concentrations in the composite (corresponding to ca. 50-1800 monomer units/NP), the formation of fractal aggregates was observed. In particular, the average fractal dimension D = 1.9 +/- 0.1 was determined for aggregates in the system with 5 x 10(-6) M polymer concentration. By contrast, in systems with polymer concentrations higher than about 1 x 10(-5) M, relatively small aggregates of Ag NPs with large interparticle distances were formed. The differences in the morphology of the composite systems with various polymer concentrations manifested themselves clearly in their SPE spectra. Furthermore, upon optical excitation with appropriate wavelengths (488.0 and 514.5 nm), the fractal aggregates acted as carriers of "hot spots", i.e. strong, localized, nanoscale optical fields, from which intense and well resolved SERS spectra of the polymer were obtained.

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“…Experimental approaches to background/baseline correction are not suitable here. [29][30][31][32][33][34] A chemometric approach (of which there are many) was preferred because this could be more easily implemented on conventional Raman systems. [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50] Morphological weighted penalized least squares (MPLS) 49 was used for baseline correction of Raman spectra because of its inherent simplicity, combined with its flexibility, suitability for automation, and effectiveness at mitigating baseline artefacts.…”

Section: Resultsmentioning

confidence: 99%

Low-Content Quantification in Powders Using Raman Spectroscopy: A Facile Chemometric Approach to Sub 0.1% Limits of Detection

et al. 2015

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Publication InformationLi, B.; Calvet, A.; Casamayou-Boucau, Y.; Morris, C.; Ryder, A.G. (2015) 'Low-content quantification in powders using Raman spectroscopy: a facile chemometric approach to sub 0.1% limits of detection'. Analytical Chemistry, 87 (6):3419-3428. PublisherAmerican Chemical Society Low-content quantification in powders using Raman spectroscopy: a facile chemometric approach to sub 0.1% limits of detection.Boyan Li, Amandine Calvet, Yannick Casamayou-Boucau, Cheryl Morris, and Alan G. Ryder* Nanoscale Biophotonics Laboratory, School of Chemistry, National University of Ireland, Galway, Galway, Ireland.* To whom correspondence should be addressed.Tel: +3539149 2943 Email: alan.ryder@nuigalway.ie ABSTRACT A robust and accurate analytical methodology for low-content (<0.1%) quantification in the solid-state using Raman spectroscopy, sub-sampling, and chemometrics was demonstrated using a piracetam-proline model. The method involved a 5-step process: collection of relatively large number of spectra (8410) from each sample by Raman mapping, meticulous data pretreatment to remove spectral artefacts, use of a 0-100% concentration range partial least squares (PLS) regression model to estimate concentration at each pixel, use of a more-accurate, reduced concentration range PLS model to accurately calculate analyte concentration at each pixel, and finally statistical analysis of all 8000+ concentration predictions to produce an accurate overall sample concentration. The relative prediction accuracy was ~2.4% for a 0.05~1.0% concentration range and the limit of detection was comparable to high performance liquid chromatography (0.03% versus 0.041%). For data pretreatment, we developed a unique cosmic ray removal method and used an automated baseline correction method, neither of which required subjective user intervention and thus were fully automatable. The method is applicable to systems, which cannot be easily analyzed chromatographically such as hydrate, polymorph, or solvate contamination.

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Raman spectra of poly(2-pyridinium hydrochloride-2-pyridylacetylene)

Cited by 11 publications

References 21 publications

An intelligent background‐correction algorithm for highly fluorescent samples in Raman spectroscopy

An intelligent background‐correction algorithm for highly fluorescent samples in Raman spectroscopy

Morphology and optical responses of SERS active π-conjugated poly(N-ethyl-2-ethynylpyridinium iodide)/Ag nanocomposite systems

Low-Content Quantification in Powders Using Raman Spectroscopy: A Facile Chemometric Approach to Sub 0.1% Limits of Detection

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