Measuring reaction kinetics in a lab-on-a-chip by microcoil NMR

A microfluidic chip with an integrated planar microcoil was developed for Nuclear Magnetic Resonance (NMR) spectroscopy on samples with volumes of less than a microliter. Real-time monitoring of imine formation from benzaldehyde and aniline in the microreactor chip by NMR was demonstrated. The reaction times in the chip can be set from 30 min down to ca. 2 s, the latter being the mixing time in the microfluidic chip. Design rules will be described to optimize the microreactor and detection coil in order to deal with the inherent sensitivity of NMR and to minimize magnetic field inhomogeneities and obtain sufficient spectral resolution.     

Periodical forcing for the control of chaos in a chemical reaction

Control of the chaotic behavior of a chemical system can be achieved perturbing periodically some control parameters of the system. This procedure based on external forcing, which is based on the phenomenon of resonance, can change a chaotic behavior into a periodical one by means of the application of a sinusoidal perturbation. In this paper, the influence of a periodical modulation added to the parameter controlling the oxygen adsorption rate in a cellular automaton (CA) model studying CO oxidation is analyzed. This CA model considers the oxidation reaction of CO on a catalytic surface, taking into account the catalyst temperature variation in order to analyze the reaction time oscillatory behavior. Simulations of the CA model exhibit chaotic and quasiperiodical behaviors, and it can be shown that the periodical forcing strategy can suppress the chaotic dynamics by means of the stabilization of periodical solutions.     

Machine learning modelling of chemical reaction characteristics: yesterday,today, tomorrow

The synthesis of the desired chemical compound is the main task of synthetic organic chemistry. The predictions of reaction conditions and some important quantitative characteristics of chemical reactions as yield and reaction rate can substantially help in the development of optimal synthetic routes and assessment of synthesis cost. Theoretical assessment of these parameters can be performed with the help of modern machine-learning approaches, which use available experimental data to develop predictive models called quantitative or qualitative structure–reactivity relationship (QSRR) modelling. In the article, we review the state-of-the-art in the QSRR area and give our opinion on emerging trends in this field.     

Electrokinetic flow control in microfluidic chips using a field-effect transistor

A field-effect transistor is developed to control flow in microfluidic chips by modifying the surface charge condition. In this investigation, zeta potential at a particular location is altered locally by applying a gate voltage, while zeta potential at other locations is maintained at its original value. This non-uniform zeta potential results in a secondary electroosmotic flow in the lateral direction, which is used for flow control in microgeometries. Here, microchannel structures and field-effect transistors are formed on polydimethylsiloxane (PDMS) using soft lithography techniques, and a micro particle image velocimetry technique is used to obtain high resolution velocity distribution in the controlled region. The flow control is observed at relatively low gate voltage (less than 50 V), and this local flow control is primarily due to current leakage through the interface between PDMS and glass layers. A leakage capacitance model is introduced to estimate the modified zeta potential for the straight channel case, and excellent agreement is obtained between the predicted and experimental zeta potential results. This leakage-current based field-effect is then applied to a T-channel junction to control flow in the branch channel. Experiments show that the amount of discharge in the branch channel can be controlled by modulating gate voltage.     

Selective and reversible control of a chemical reaction with narrow-band infrared radiation: HXeCC radical in solid xenon

The light-induced H + XeC2 <--> HXeCC reaction is studied in solid Xe, and the full optical control of this reaction is demonstrated. By narrow-band excitation in the IR spectral region, HXeCC radicals can be decomposed to a local metastable configuration and then selectively recovered by resonant excitation of the XeC2 vibrations. The novel recovery process is explained by short-range mobility of the reagents promoted by vibrational energy redistribution near the absorbing XeC2 molecule. This means that a chemical reaction can be selectively promoted in a desired place where the chosen absorber locates. The obtained results make a strong case of solid-state reactive vibrational excitation spectroscopy of weak radiationless transitions.     

Development and optimization of a lab-on-a-chip device for the measurement of trace nitrogen dioxide gas in the atmosphere

We propose the use of lab-on-a-chip technology for measuring gaseous chemical pollutants, and describe the development of a microchip for the detection of nitrogen dioxide (NO2) in air. A microchip fabricated from quartz glass has been developed for handling the following three functions, gas absorption, chemical reaction and fluorescence detection. Channels constructed in the microchip were covered with porous glass plates, allowing nitrogen dioxide to penetrate into the triethanolamine (TEA) flowing within the microchannel beneath. The nitrogen dioxide was then mixed with TEA and reacted with a suitable fluorescence reagent in the chemical reaction chamber in the microchip. The reacted solution was then allowed to flow into the fluorescence detection area to be excited by an ultraviolet light-emitting diode (UV-LED), and the fluorescence was detected using a photomultiplier tube (PMT). The reaction time, reagent concentration, pH, flow rate and other measurement conditions were optimised for analysis of nitrogen dioxide in air. Preliminary studies with standardized test solutions revealed quantitative measurements of nitrite ion (NO2-), which corresponded to atmospheric nitrogen dioxide in the range of 10-80 ppbv.     

An automatic reaction control chemical ionization technique in ion trap detector for quantitative plasma profding of arecoline in treated alzheimer patients

An automatic reaction control chemical ionization technique in an ion trap detector (lTD) was used to quantitate the levels of the cholinergic drug, arecoline, in plasma of treated patients with Alzheimer’s disease. The chemical ionization reaction was carried out with acetonitrile. The protonated molecules of arecoline (m I z 156) and the internal standard, homoarecoline (m / z 170), were monitored. Human plasma samples were extracted with a readily evaporable solvent mixture, the residues reconstituted and injected along with a tertiary amine-carrier into a capillary gas chromatograph interfaced with the ITD. Standard curves for plasma-extracted arecoline between 20-ng/mL and 156-pg/mL levels were linear (r> 0.9980). Satisfactory precision (relative standard deviation < 20%) and accuracy (relative error < 15%) at the limit of quantitation, 156 pg/mL arecoline, were achieved. Optimal conditions for handling of blood samples obtained by venipuncture were determined. The assay was successfully applied for the therapeutic monitoring of Alzheimer patients treated intravenously with arecoline.     

Computational modelling of radiative Maxwell fluid flow over a stretching sheet containing nanoparticles with chemical reaction

In the presence of thermal radiation, heat generation/absorption, and chemical reaction, the heat and mass transmission characteristics of a 2-D electrically conducting incompressible Maxwell fluid past a stretched sheet have been examined. There are various real-world applications for this issue, notably the extrusion of polymers and metal thinning. The transport equations take into account Brownian motion as well as thermophoresis when there is a chemical reaction involved. By making use of the relevant similarity variables, the PDEs that govern the stream and the boundary conditions that go along with them may be non-dimensionalized. The ensuing transformed ODEs are solved using the fourth- and fifth-order Runge-Kutta-Fehlberg scheme. It has been determined and quantitatively examined how the different embedded thermo-physical factors influence the velocity, temperature, and concentration. A case study comparison between our findings and those published in the literature reveals a high degree of agreement. Raising the value of the chemical reaction parameter causes a narrowing of the concentration distribution while increasing the temperature causes thermal radiation to have a greater impact. As the quantity of Nt increases, the thickness of the boundary layer grows, causing the surface temperature to increase, ensuing in a temperature increase.     

Chemometrics: the issues of measurement and modelling

In this paper, two spectral data sets have been used to illustrate the importance of maintaining chemical information whilst generating predictive multivariate calibration models. The first data set is based on 26 duplicate UV/VIS spectra for four meal ions (Fe, Ni, Co, Cu) present at varying concentrations in aqueous solution. Spectra were collected across the range 180–800 nm at a resolution of 3.5 nm generating 211 data points for each sample. Calibration was carried out using multiple linear regression (MLR) and a K-matrix approach to demonstrate the advantages the latter method has in describing real spectral features. In addition, the limitation of MLR in accommodating noise and spectral overlap in the data is also illustrated. The second data set based on NIR spectroscopy, was generated using a four-level 2 factor Factorial design strategy and consisted of two additives present at a range of concentrations in an aqueous caustic system, with the spectra being collected over the range 10,000–3000 cm⁻¹. Whilst a conventional partial least squares (PLS) model was applied to the data, it was through the use of variable selection (VS) prior to PLS and the application of weighted ridge regression (WRR) techniques that the need to develop chemometric methodology which intuitively reflected chemical information has been demonstrated. The results will also illustrate how a poorly designed experimental design protocol and missing data can limit the performance of the calibration models generated. The aims of this paper are not to prescribe ideal calibration methodology but rather to demonstrate the relevance of selecting multivariate calibration methodology that relates more to the chem rather than just the metrics in chemometrics.     

Moisture absorption measurement and modelling of a cellulose acetate

Cellulose - With a view toward the application of highly hygroscopic polymers as a humidity responsive self-actuator, the evaluation of the real time moisture concentration in the material becomes...     

Tutorial: The modelling of chemical processes

The modelling of chemical processes entails the computation of the concentration profiles of all reaction species as a function of the reaction time. The basis for the calculations is the system of differential equations (ODE's) that is defined by the reaction mechanism. Most textbooks on chemical kinetics concentrate on those few reaction mechanisms that lead to ODE's with explicit solutions. In this tutorial, we demonstrate that numerical integration is a viable alternative, that it can be applied to any mechanism, and that it is easy to do so. Matlab example programs illustrate the concepts and they allow the reader to explore the effects of changing conditions such as initial concentrations or rate constants etc. Example reaction mechanisms include a zero-th order enzymatic reaction and reactions at non-constant temperature and pH.     

原位拉曼光谱技术用于反应和结晶控制研究进展

随着高分辨光谱技术和原位技术的发展,原位拉曼光谱的研制促进一系列原位微观物理化学行为及机理的研究,对材料的设计和应用有着重要意义.本文从拉曼光谱的原理及拉曼信号的影响因素出发,概述原位拉曼光谱技术用于反应和结晶控制的具体实例,总结原位拉曼光谱在化学反应和溶液结晶中晶体多态性、中间体和产物微观结构、化学键合模式等方面的研究进展,为研究反应及结晶机理提供重要线索,并对原位拉曼光谱技术的发展趋势进行了预测.     

Treatment of a class-in-class modelling problem in chemical pattern recognition

A supervised learning procedure is described for the detection and characterization of classes which overlap or are located within other classes. The UNEQ classifier proposed by Derde and Massart is extended by applying tests for multivariate homogeneity and homoscedasticity, principal components analysis of each class box, and Boolean-type decision rules. The extended algorithm is suitable for class-in-class modelling. The procedure is applied to chemical data for an environmental problem involving an industrial source of emission and its immission effects.     

New developments in quantitative polymerase chain reaction applied to control the quality of heparins

Heparin is a widely used intravenous anticoagulant comprised of a very complex mixture of glucosaminoglycan chains, mainly derived from porcine intestinal mucosa. Recent contamination of heparin with oversulfated (OS) chondroitin sulfate resulted in a significant number of deaths, triggering a rapid revision of product monographs and the introduction of new analytical methods to limit as far as possible the chances of another occurrence of such a phenomenon. The distribution of heparin-processing units across the globe prevents their complete fool-proof auditing. Therefore, the implementation of additional orthogonal analytical techniques for quality control (QC) of heparin batches is highly important. We perform routine quantitative polymerase chain reaction (Q-PCR) release tests to confirm the quality of all crude heparin batches received by sanofi-aventis. The routine test used provides information on the animal species of origin as requested by the US Pharmacopoeia (USP) and European Pharmacopoiea monographs. Here, we demonstrate that the Q-PCR test is inhibited by OS glycosaminoglycans at concentrations as low as 0.5% (w/w versus heparin) and can be used as an additional safeguard to monitor levels of potentially harmful contaminants without any increased workload. In response to a request from the USP, we also describe the development of a Q-PCR method for monitoring nucleotidic impurities in pure heparin, which is able to detect amplifiable DNA at concentrations lower than 0.1 ng DNA per milligram of heparin. This increased sensitivity makes this modified Q-PCR method a potential candidate for inclusion as a QC requirement in future monographs.     

Root-aligned SMILES: a tight representation for chemical reaction prediction

Chemical reaction prediction, involving forward synthesis and retrosynthesis prediction, is a fundamental problem in organic synthesis. A popular computational paradigm formulates synthesis prediction as a sequence-to-sequence translation problem, where the typical SMILES is adopted for molecule representations. However, the general-purpose SMILES neglects the characteristics of chemical reactions, where the molecular graph topology is largely unaltered from reactants to products, resulting in the suboptimal performance of SMILES if straightforwardly applied. In this article, we propose the root-aligned SMILES (R-SMILES), which specifies a tightly aligned one-to-one mapping between the product and the reactant SMILES for more efficient synthesis prediction. Due to the strict one-to-one mapping and reduced edit distance, the computational model is largely relieved from learning the complex syntax and dedicated to learning the chemical knowledge for reactions. We compare the proposed R-SMILES with various state-of-the-art baselines and show that it significantly outperforms them all, demonstrating the superiority of the proposed method.

We propose the root-aligned SMILES (R-SMILES), which specifies a tightly aligned one-to-one mapping between the product and the reactant SMILES for more efficient sequence-based synthesis prediction.