Experimental characterization, machine learning analysis and computational modelling of the high effective inhibition of copper corrosion by 5-(4-pyridyl)-1,3,4-oxadiazole-2-thiol in saline environment.

An oxadiazole derivative with functional groups favouring its adsorption on copper surface, namely 5-(4-Pyridyl)-1,3,4-oxadiazole-2-thiol, has been explored as potential inhibitor of copper corrosion in 3.5 wt.% NaCl. Electrochemical evaluation by electrochemical impedance spectroscopy, potentiodynamic polarization and SVET reveals inhibition efficiencies exceeding 99%. Surface microscopy inspection and spectroscopic analysis by Raman, SEM-EDX and XPS highlight the formation of a compact barrier film responsible for long-lasting protection, that is mainly composed of the organic molecules. Machine Learning algorithms used in combination with Raman spectroscopy data were used successfully for the first time in corrosion studies to allow discrimination between corroded and inhibitor-protected metal surfaces. Quantum Chemistry calculations in aqueous solution and Molecular Dynamic studies predict a strong interaction between copper and the thiolate group and an extensive coverage of the metal surface, responsible for the excellent protection against corrosion

Synthèse et stéréochimie de systèmes 3.7-dioxa-1-azabicyclo[3.3.0]octaniques fonctionnalisés (Etude la métallation de bis-diazines et de diazines substituées par des groupements chélatants)

L ensemble des travaux de recherche de cette thèse constitue des avancées dans différents domaines pour la synthèse et la fonctionnalisation des systèmes bicycliques 3.7-DiOxa-r-AzaBicyclo[3.3.0]-c-5-Octanes (DOABO) et des diazines et benzo-diazines. Le chapitre 1 est consacré à une étude bibliographique sur la synthèse, la stéréochimie, la fonctionnalisation et les applications des DOABO et des diazines. Le chapitre 2 est relatif à la synthèse et à la stéréochimie d une nouvelle classe de composés, les a-(3.7-dioxa-r-1-azabicyclo[3.3.0]-oct-c-5-ylmethoxy)-di(s-tri)azines. La stéréochimie de ces composés est discutée en terme de chiralité conformationnelle du système DOABO par RMN dynamique haut-champ et par RX. Le chapitre 3 est consacré à l étude de la métallation de diazines utilisant le groupe DOABO comme groupe ortho-directeur. L efficacité de ce groupe a été comparée à celle d autres groupes directeurs. Le chapitre 4 concerne une étude de la fonctionnalisation des structures biaryliques par métallation et utilise le cycle pyridinique comme groupe ortho-directeur.This work deals with the synthesis, the stereochemical analysis and functionalisation of compounds bearing the 3.7-dioxa-r-1-azabicyclo[3.3.0]-oct-c-5-ylmethoxy heterocyclic system such as substituent at the a position of a p-deficient system such as (benzo)diazines or s-triazines. The first chapter deals with the bibliography and interest of DOABO heterocycles and diazines. The second chapter presents the synthesis and stereochemistry of a new class of compounds such as a-(3.7-dioxa-r-1-azabicyclo[3.3.0]-oct-c-5-ylmethoxy)-di(s-tri)azines. The stereochemistry of this new series is discussed in terms of conformational chirality of the DOABO system, found different in solution (1H RMND)against solid state (X Ray Diffractometry), meso vs. chiral forms respectively. The third chapter presents the functionalisation of a-(3.7-dioxa-r-1-azabicyclo[3.3.0]-oct-c-5-ylmethoxy)-(beno)diazines via regioselective Directed ortho-metallation (DoM) reaction using lithiated bases. The fourth chapter describes the synthesis of p-deficient biarylic compounds of type bis(a-methoxy)diazines and 2-pyridyldiazines. They are available by directed ortho-(trans)metallation followed by Negishi or Stille cross-coupling. The functionalisation via metallation of 2-pyridyldiazines occurs on the diazine site, at the ortho-position with respect to Ar-Ar bond.ROUEN-BU Sciences (764512102) / SudocROUEN-BU Sciences Madrillet (765752101) / SudocSudocFranceF

The Development of Honey Recognition Models Based on the Association between ATR-IR Spectroscopy and Advanced Statistical Tools

The newly developed prediction models, having the aim to classify Romanian honey samples by associating ATR-FTIR spectral data and the statistical method, PLS-DA, led to reliable differentiations among the samples, in terms of botanical and geographical origin and harvesting year. Based on this approach, 105 out of 109 honey samples were correctly attributed, leading to true positive rates of 95% and 97% accuracy for the harvesting differentiation model. For the botanical origin classification, 83% of the investigated samples were correctly predicted, when four honey varieties were simultaneously discriminated. The geographical assessment was achieved in a percentage of 91% for the Transylvanian samples and 85% of those produced in other regions, with overall accuracy of 88% in the cross-validation procedure. The signals, based on which the best classification models were achieved, allowed the identification of the most significant compounds for each performed discrimination

Edible Oils Differentiation Based on the Determination of Fatty Acids Profile and Raman Spectroscopy—A Case Study

This study proposes a comparison between two analytical techniques for edible oil classification, namely gas-chromatography equipped with a flame ionization detector (GC-FID), which is an acknowledged technique for fatty acid analysis, and Raman spectroscopy, as a real time noninvasive technique. Due to the complexity of the investigated matrix, we used both methods in connection with chemometrics processing for a quick and valuable evaluation of oils. In addition to this, the possible adulteration of investigated oil varieties (sesame, hemp, walnut, linseed, sea buckthorn) with sunflower oil was also tested. In order to extract the meaningful information from the experimental data set, a supervised chemometric technique, namely linear discriminant analysis (LDA), was applied. Moreover, for possible adulteration detection, an artificial neural network (ANN) was also employed. Based on the results provided by ANN, it was possible to detect the mixture between sea buckthorn and sunflower oil

Combined Electrochemical, Raman Analysis and Machine Learning Assessments of the Inhibitive Properties of an 1,3,4-Oxadiazole-2-Thiol Derivative against Carbon Steel Corrosion in HCl Solution

The inhibiting properties of 5-(4-pyridyl)-1,3,4-oxadiazole-2-thiol (PyODT) on the corrosion of carbon steel in 1.0 M HCl solution were investigated by potentiodynamic polarization, electrochemical impedance spectroscopy, Raman spectroscopy, and SEM-EDX analysis. An approach based on machine learning algorithms and Raman data was also applied to follow the carbon steel degradation in different experimental conditions. The electrochemical measurements revealed that PyODT behaves as a mixed-type corrosion inhibitor, reaching an efficiency of about 93.1% at a concentration of 5 mM, after 1 h exposure to 1.0 M HCl solution. Due to the molecular adsorption and structural organization of PyODT molecules on the C-steel surface, higher inhibitive effectiveness of about 97% was obtained at 24 h immersion. The surface analysis showed a significantly reduced degradation state of the carbon steel surface in the presence of PyODT due to the inhibitor adsorption revealed by Raman spectroscopy and the presence of N and S atoms in the EDX spectra. The combination of Raman spectroscopy and machine learning algorithms was proved to be a facile and reliable tool for an incipient identification of the corrosion sites on a metallic surface exposed to corrosive environments

Botanical Origin Assessment of Honey Based on ATR-IR Spectroscopy: A Comparison between the Efficiency of Supervised Statistical Methods and Artificial Intelligence

Food authenticity control represents a constant concern nowadays, and against this background, new means of food fraud detection are developed by research and control laboratories. Among the most accessible analytical methods in this regard, attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy proved to be an effective tool, being rapid, cost-effective, and not requiring solvent use. However, the generated experimental data need to be further processed in an efficient manner in order to be able to accurately assess the authenticity of a certain product. The temptation to pass some more available honey varieties as rarer ones might exist and in order to detect these types of miss labeling, we proposed in this study the development of new recognition models based on supervised chemometric models and artificial intelligence. In this way a comparison between the models’ capabilities constructed based on the association between ATR-IR spectroscopy with partial least squares discriminant analysis (PLS-DA) and support vector machines (SVM), respectively, was performed. The most efficient models for the individual botanical differentiation were developed by applying SVM on the significant spectral markers, determined through a supervised method

Botanical Origin Assessment of Honey Based on ATR-IR Spectroscopy: A Comparison between the Efficiency of Supervised Statistical Methods and Artificial Intelligence

Food authenticity control represents a constant concern nowadays, and against this background, new means of food fraud detection are developed by research and control laboratories. Among the most accessible analytical methods in this regard, attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy proved to be an effective tool, being rapid, cost-effective, and not requiring solvent use. However, the generated experimental data need to be further processed in an efficient manner in order to be able to accurately assess the authenticity of a certain product. The temptation to pass some more available honey varieties as rarer ones might exist and in order to detect these types of miss labeling, we proposed in this study the development of new recognition models based on supervised chemometric models and artificial intelligence. In this way a comparison between the models’ capabilities constructed based on the association between ATR-IR spectroscopy with partial least squares discriminant analysis (PLS-DA) and support vector machines (SVM), respectively, was performed. The most efficient models for the individual botanical differentiation were developed by applying SVM on the significant spectral markers, determined through a supervised method

The Influence of Ag⁺/Ti⁴⁺ Ratio on Structural, Optical and Photocatalytic Properties of MWCNT–TiO₂–Ag Nanocomposites

In this paper, we propose a simple procedure to obtain multi-walled carbon nanotubes (MWCNTs) decorated with TiO2–Ag nanoparticles (MWCNT–TiO2–Ag). The MWCNTs were decorated with TiO2–Ag via combined functionalization with –OH and –COOH groups and a polymer-wrapping technique using poly(allylamine)hydrochloride (PAH). TiO2-modified Ag nanoparticles were synthesized via the Pechini method using a mixture of acetylacetonate-modified titanium (IV) isopropoxide with silver nitrate (with Ag+/Ti4+ atomic ratios of 0.5%, 1.0%, 1.5%, 2.0%, and 2.5%) and L(+)-ascorbic acid as reducing agents. XRD analysis revealed the formation of nanocomposites containing CNT, anatase TiO2, and Ag. The presence of nanoparticles on the MWCNT surfaces was determined using TEM. The morphology of the TiO2–Ag nanoparticles on the MWCNT surfaces was also determined using TEM. UV–Vis investigations revealed that an increase in the ratio between Ag+ and Ti4+ decreased the band gap energy of the samples. The characteristic vibrations of the TiO2, Ag, and C atoms of the graphite were identified using Raman spectroscopy. The photocatalytic activity of the MWCNT–TiO2–Ag nanocomposite was assessed by examining the degradation of Allura Red (E129) aqueous solution under UV irradiation. The dye photodegradation process followed a pseudo-first-order kinetic with respect to the Langmuir–Hinshelwood reaction mechanism. The spin-trapping technique evidenced that •O2− was the main species generated responsible for the Allura Red degradation

Eco-Friendly Nitrogen-Doped Graphene Preparation and Design for the Oxygen Reduction Reaction

Four N-doped graphene materials with a nitrogen content ranging from 8.34 to 13.1 wt.% are prepared by the ball milling method. This method represents an eco-friendly mechanochemical process that can be easily adapted for industrial-scale productivity and allows both the exfoliation of graphite and the synthesis of large quantities of functionalized graphene. These materials are characterized by transmission and scanning electron microscopy, thermogravimetry measurements, X-ray powder diffraction, X-ray photoelectron and Raman spectroscopy, and then, are tested towards the oxygen reduction reaction by cyclic voltammetry and rotating disk electrode methods. Their responses towards ORR are analysed in correlation with their properties and use for the best ORR catalyst identification. However, even though the mechanochemical procedure and the characterization techniques are clean and green methods (i.e., water is the only solvent used for these syntheses and investigations), they are time consuming and, generally, a low number of materials can be prepared, characterized and tested. In order to eliminate some of these limitations, the use of regression learner and reverse engineering methods are proposed for facilitating the optimization of the synthesis conditions and the materials’ design. Thus, the machine learning algorithms are applied to data containing the synthesis parameters, the results obtained from different characterization techniques and the materials response towards ORR to quickly provide predictions that allow the best synthesis conditions or the best electrocatalysts’ identification

Preparation of Pt electrocatalyst supported by novel, Ti(1−x)MoxO2-C type of composites containing multi-layer graphene

Ball milling is a relative simple and promising technique for preparation of inorganic oxide–carbon type of composites. Novel TiO2-C and Ti0.8Mo2O2-C type of composites containing multi-layer graphene were prepared by ball milling of graphite in order to get electrocatalyst supports for polymer electrolyte membrane fuel cells. Starting rutile TiO2 was obtained from P25 by heat treatment. Carbon-free Ti0.8Mo2O2 mixed oxide, prepared using our previously developed multistep sol–gel method, does not meet the requirements for materials of electrocatalyst support, therefore parent composites with Ti0.8Mo2O2/C = 75/25, 90/10 and 95/5 mass ratio were prepared using Black Pearls 2000. XRD study of parent composites proved that the oxide part existed in rutile phase which is prerequisite of the incorporation of oxophilic metals providing CO tolerance for the electrocatalyst. Ball milling of TiO2 or parent composites with graphite resulted in catalyst supports with enhanced carbon content and with appropriate specific surface areas. XRD and Raman spectroscopic measurements indicated the changes of graphite during the ball milling procedure while the oxide part remained intact. TEM images proved that platinum existed in the form of highly dispersed nanoparticles on the surface of both the Mo-free and of Mo-containing electrocatalyst. Electrocatalytic performance of the catalysts loaded with 20 wt% Pt was studied by cyclic voltammetry, COads-stripping voltammetry done before and after the 500-cycle stability test, as well as by the long-term stability test involving 10,000 polarization cycles. Enhanced CO tolerance and slightly lower stability comparing to Pt/TiO2-C was demonstrated for Pt/Ti0.8Mo2O2-C catalysts