UV VIS AND RESONANCE RAMAN SPECTROELECTROCHEMICAL PROPERTIES OF TRANSITION-METAL CENTERS IMMOBILIZED WITHIN A POLY(AMINO ACID) MATRIX - ILLUSTRATED WITH AN IRON PORPHYRIN

An iron prophyrin complex has been immobilized on the surfaces of platinum, silver, and indium doped-tin oxide coated glass by using the poly(gamma-ethyl L-glutamate)-N-(3-aminopropyl)imidazole derivative 1 as a linking agent, thus allowing-the surface-enhanced resonance Raman and UV-VIS absorption spectra and electrochemical properties of the porphyrin to be studied in solvents in which it is not normally soluble.

Predicting organic acid concentration from UV/vis spectrometry measurements - A comparison of machine learning techniques

The concentration of organic acids in anaerobic digesters is one of the most critical parameters for monitoring and advanced control of anaerobic digestion processes. Thus, a reliable online-measurement system is absolutely necessary. A novel approach to obtaining these measurements indirectly and online using UV/vis spectroscopic probes, in conjunction with powerful pattern recognition methods, is presented in this paper. An UV/vis spectroscopic probe from S::CAN is used in combination with a custom-built dilution system to monitor the absorption of fully fermented sludge at a spectrum from 200 to 750 nm. Advanced pattern recognition methods are then used to map the non-linear relationship between measured absorption spectra to laboratory measurements of organic acid concentrations. Linear discriminant analysis, generalized discriminant analysis (GerDA), support vector machines (SVM), relevance vector machines, random forest and neural networks are investigated for this purpose and their performance compared. To validate the approach, online measurements have been taken at a full-scale 1.3-MW industrial biogas plant. Results show that whereas some of the methods considered do not yield satisfactory results, accurate prediction of organic acid concentration ranges can be obtained with both GerDA and SVM-based classifiers, with classification rates in excess of 87% achieved on test data.

Engineering of Vis-NIR Metamaterials towards Revolutionary Ultrasensitive and Versatile Plasmonic Biosensors

By enabling subwavelength light localization and strong electromagnetic field enhancement, plasmonic biosensors have opened up a new realm of possibilities for a broad range of chemical and biological sensing applications owing to their label-free and real-time attributes. Although significant progress has been made, many fundamental and practical challenges still remain to be addressed. For instance, the plasmonic biosensors are nonselective sensing platforms; they are not well-suited to provide information regarding conformation or chemical fingerprint of unknown biomolecules. Furthermore, tunability of the plasmonic resonance in visible frequency regime is still limited; this will prevent their efficient and reproducible exploitation in single-molecule sensitivity. Here, we show that by engineering geometry of plasmonic metamaterials,1 consisting of periodic arrays of artificial split-ring resonators (SRRs), the plasmonic resonance of metamaterials could be tuned to visible-near infrared regimes (Vis-NIR) such that it allows parallel acquisition of optical transmission and highly surface-enhanced Raman (SERS) spectra from large functionalized SRR arrays. The Au SRRs were designed in form of alphabet letters (U, V, S, H, Y) with various line width (from 80 to 30 nm). By tailoring their size and shape, plasmonic resonance wavelength of the SRRs could be actively tuned so that it gives the strongest SERS effect under given excitation energy and polarization for biological and organic molecules. On the other hand, the plasmonic tunability was also achieved for a given SRR pattern by tuning the laser wavelength to obtain the highest electromagnetic field enhancement. The geometry- and laser-tunable channels typically provide an electromagnetic field enhancement as high as 20 times. This will provide the basis of versatile and multichannel devices for identification of different conformational states of Guanine-rich DNA, detection of a cancer biomarker nucleolin, and femtomolar sensitivity detection of food and drink additives. These results show that the tunable Vis-IR metamaterials are very versatile biosensing platforms and suggest considerable promise in genomic research, disease diagnosis, and food safety analysis.

Label-free Analysis of Conformational Dynamics and Molecular Interaction of Biomolecules by Vis-NIR Metasensor

Analysis of binding recognition and conformation of biomolecules is of paramount important in understanding of their vital functions in complex biological systems. By enabling sub-wavelength light localization and strong local field enhancement, plasmonic biosensors have become dominant tools used for such analysis owing to their label-free and real-time attributes1,2. However, the plasmonic biosensors are not well-suited to provide information regarding conformation or chemical fingerprint of biomolecules. Here, we show that plasmonic metamaterials, consisting of periodic arrays of artificial split-ring resonators (SRRs)3, can enable capabilities of both sensing and fingerprinting of biomolecules. We demonstrate that by engineering geometry of individual SRRs, localized surface plasmon resonance (LSPR) frequency of the metamaterials could be tuned to visible-near infrared regimes (Vis-NIR) such that they possess high local field enhancement for surface-enhanced Raman scattering spectroscopy (SERS). This will provide the basis for the development of a dual mode label-free conformational-resolving and quantitative detection platform. We present here the ability of each sensing mode to independently detect binding adsorption and to identify different conformational states of Guanine (G)-rich DNA monolayers in different environment milieu. Also shown is the use of the nanosensor for fingerprinting and detection of Arginine-Glycine-Glycine (RGG) peptide binding to the G-quadruplex aptamer. The dual-mode nanosensor will significantly contribute to unraveling the complexes of the conformational dynamics of biomolecules as well as to improving specificity of biodetection assays that the conventional, population-averaged plasmonic biosensors cannot achieve.

Wide-ranging alterations in the brain fatty acid complement of subjects with late Alzheimer’s disease as detected by GC-MS

Disturbed lipid metabolism is a well-established feature of human Alzheimer’s disease (AD). The present study used gas chromatography-mass spectrometry (GC-MS) analysis of fatty acid methyl esters (FAMES) to profile all detectable fatty acid (FA) species present in post-mortem neocortical tissue (Brodmann 7 region). Quantitative targeted analysis was undertaken from 29 subjects (n=15 age-matched controls; n=14 late-stage AD). GC-MS analysis of FAMES detected a total of 24 FAs and of these, 20 were fully quantifiable. The results showed significant and wide ranging elevations in AD brain FA concentrations. A total of 9 FAs were elevated in AD with cis-13,16-docosenoic acid increased most (170%; P=0.033). Intriguingly, docosahexanoic acid (DHA; C22:6) concentrations were elevated (47%; P=0.018) which conflicts with the findings of others (unaltered or decreased) in some brain regions after the onset of AD. Furthermore, our results appear to indicate that subject gender influences brain FA levels in AD subjects (but not in age-matched control subjects). Among AD subjects 7 FA species were significantly higher in males than in females. These preliminary findings pinpoint FA disturbances as potentially important in the pathology of AD. Further work is required to determine if such changes are influenced by disease severity or different types of dementia.