Performances under representative pressure and temperature conditions of the gas chromatography-mass spectrometry space experiment to investigate Titan's atmospheric composition

In the frame of the calibration of the aerosol collector and pyrolyser, and gas chromatography-mass spectrometry experiments of the Huygens probe arrived at Titan, systematic experimental studies were led to estimate the influence of the operating conditions on the analyses that should have been achieved in the Titan's atmosphere. The primary objective of this study was to estimate the influence of operating conditions variations induced by (i) instrumental modifications made shortly before the probe launch which can have changed the operating pressures; (ii) the change of the probe environmental conditions (pressure, temperature) during its descent in the atmosphere; (iii) a possible deviation of pressure and temperature regulations from their nominal values because of the long journey of the instrument in space, or of other external events. The secondary objective of this work was to create an analytical database that can be used as a reference to treat the chromatograms obtained in situ, and help to identify chromatographically the analyzed species, complementary to mass spectrometry. Beyond the application to a specific instrument, this work was also useful to experimentally estimate the fundamental evolution of the separation as a function of the changes of operating conditions with time. The obtained results show (i) the significant influence of inlet and outlet pressure variation on the time of analysis, but not on the separation power. It thus enables to significantly shorten the analysis duration, and thus to analyze more compounds within the fixed time of analysis of the instrument; (ii) the significant influence of temperature on the retention. In this frame, the enthalpies of exchange between the gas phase and the stationary phase of the species were determined to be used to retrieve the analyzed species in case of deviation of the operating temperature; (iii) that the possible aging of the columns does not have influence on the columns efficiency and separation power; (iv) the analytical capabilities of the gas chromatography-mass spectrometry experiment within operating conditions representative of those encountered in situ. Finally, in spite of possible operating condition changes, it is shown that results coming from the gas chromatograph-mass spectrometer experiment, which are currently under analysis, could bring important information on the Titan's atmosphere and its history.     

New approach to Tian’s equation applied to heat conduction and liquid injection calorimeters

In the calorimeters used for the determination of thermodynamical properties of liquid environments, the mixture takes place when injecting liquid in the mixture zone, this injection incorporates an additional calorific power that is a function of the volumetric heat capacity of the injected liquid and the injection flow. In this article, it is rewritten Tian??s equation including this additional power to relate correctly the experimental output to the mixture enthalpy. It is applied Tian??s equation, once it has been corrected, to two types of calorimeters: flow-microcalorimeters and isothermal titration calorimeters. In this second case, it has been taken into account the classical operating mode (titration) and the continuous liquid injection mode. Tian??s equation, completed with all the energetic terms additional to the mixture process, is of great interest for the scientific and academic community because it allows to explain, in a simple and effective way, the operation of these instruments.     

The use of artificial neural networks for the selection of the most appropriate thermal parameters and for the classification of a set of phenylcarbamic acid derivates

The objective of this work was to apply artificial neural networks (ANNs) to the classification group of 43 derivatives of phenylcarbamic acid. To find the appropriate clusters Kohonen topological maps were employed. As input data, thermal parameters obtained during DSC and TG analysis were used. Input feature selection (IFS) algorithms were used in order to give an estimate of the relative importance of various input variables. Additionally, sensitivity analysis was carried out to eliminate less important thermal variables. As a result, one classification model was obtained, which can assign our compounds to an appropriate class. Because the classes contain groups of molecules structurally related, it is possible to predict the structure of the compounds (for example the position of the substitution alkoxy group in the phenyl ring) on the basis of obtained parameters.     

Dead-end ultrafiltration in hollow fiber modules: Module design and process simulation

Ultrafiltration in a hollow-fiber module operating with outside-in and dead-end flow at a constant flow rate was simulated using a model that takes into account the longitudinal pressure drops inside the fibers and within the fiber bundle. The model considers both the filtration phase during which the membrane is fouled by the formation of a filter cake and the backwash phase in which it is cleaned, so as to predict the net rate of production of the module during an operating cycle.The results show that there is a combination of packing density and fiber diameter that gives a maximum net flow rate. Furthermore, this model allows the influence of operating conditions and feed properties on the module performance to be estimated. This can be used to determine how operating parameters must be modified when there is a change in the feed properties.     

Determination of physical properties of bitumens by use of near-infrared spectroscopy with neural networks. Joint modelling of linear and non-linear parameters

The fact that bitumens behave as non-Newtonian fluids results in non-linear relationships between their near-infrared (NIR) spectra and the physico-chemical properties that define their consistency (viz. penetration and viscosity). Determining such properties using linear calibration techniques [e.g. partial least-squares regression (PLSR)] entails the previous transformation of the original variables by use of non-linear functions and employing the transformed variables to construct the models. Other properties of bitumens such as density and composition exhibit linear relationships with their NIR spectra. Artificial neural networks (ANNs) enable modelling of systems with a non-linear property-spectrum relationship; also, they allow one to determine several properties of a sample with a single model, so they are effective alternatives to linear calibration methods. In this work, the ability of ANNs simultaneously to determine both linear and non-linear parameters for bitumens without the need previously to transform the original variables was assessed. Based on the results, ANNs allow the simultaneous determination of several linear and non-linear physical properties typical of bitumens.     

Influence of “controlled processing conditions” on the solidification of iPP,PET and PA6

In this work reliable experimental data for three semicrystalline polymers (iPP, PA6, PET) crystallised under pressure and high cooling rates are supplied. These results were achieved on the basis of a model experiment where drastic “controlled” solidification conditions are applied. The final objective was to quantify the effect of two typical operating conditions (pressure and cooling rate) on the final properties and morphology of the obtained product. The influence of processing conditions on some macroscopically relevant properties, such as density and micro hardness is stressed, together with the influence of processing conditions on the product morphology, investigated by means of Wide Angle X-Ray Scattering (WAXS). Results on the iPP samples display a decrease of density and micro hardness, due to the pressure increase, in a wide range of cooling rates (from 0.01 to 20°C/s). PET samples exhibit an opposite behaviour with density and micro hardness increasing at higher pressures in the whole range of cooling rates investigated. PA6 samples behave similarly to PET displaying a less significant increase of density and micro hardness with pressure than PET samples.     

Analyses of gibberellins by capillary electrophoresis-mass spectrometry combined with solid-phase extraction

A new, simple and rapid capillary electrophoresis-mass spectrometry (CE-MS) method, using a cationic polymer-coated capillary to reverse electroosmotic flow, is proposed and validated for the separation and simultaneous quantification of 11 gibberellins (GAs). Under optimum conditions, a baseline separation of 11 GAs, including GA1, GA3, GA4, GA5, GA6, GA7, GA13, GA19, GA20, GA24 and GA53 was accomplished within 25 min using 70 mM ammonium formate/formic acid buffer (pH 3.8) and 2% (v/v) acetonitrile with -25 kV as the separation voltage. Satisfactory results were obtained in terms of linearity (R2 between 0.984 and 0.995), precision (RSD of migration time below 0.8%) and sensitivity (LOD between 0.31 and 1.02 microM). Furthermore, a novel solid-phase extraction (SPE) procedure was developed for the pre-concentration and purification of GAs using Oasis MAX cartridges. The combination of SPE and CE-MS approach was applied to screen for endogenous GAs present in coconut (Cocos nucifera L.) water sample. To illustrate the applicability of the method, GA1 and GA3 were successfully detected and quantified in coconut water. Finally, the GA1 and GA3 identities were further unequivocally confirmed by CE-tandem MS experiments operating in the multiple reaction monitoring mode.     

Computational evaluation of mass filter acceptance and transmittance influenced by developing fields: An application of the plane method to investigate prefilter efficacy for rectangular wave operated mass filters

The acceptance of quadrupole mass filters is improved when the alternating current (AC) and direct current (DC) fields are developed separately. Physically, this is achieved when a short RF only quadrupole (prefilter) is situated directly ahead of the mass filter. The acceptance gained by a system operating with a prefilter can be observed as an increase in sensitivity over conventional operation. Frequency dynamic duty cycle based rectangular waveform driven (rectangular wave) mass filters, a recent development, currently do not operate with prefilters. Little is known about the influence of duty cycle changes on the acceptance of rectangular wave mass filters. The sensitivity gain seen by conventional systems operating with prefilters indicates that the sensitivity of duty cycle based rectangular wave systems should increase comparably. The objective of this work was to determine prefilter efficacy for nonspecific rectangular wave mass filter systems. In this work, the plane method of acceptance was used to model the change to the acceptance and transmittance of sine and rectangular waveform driven mass filters under different modes of field development. Both systems indicated a fourfold increase in sensitivity when the mass filtering DC or duty cycle was delayed.     

Simulation and optimization of a supercritical extraction process for recovering provitamin A

In this work, a simulation procedure of a supercritical extraction process was developed through the use of the commercial simulator HYSYS^TM (Hyprotech Ltd.), adapting the existing units to the operating conditions typical of the supercritical extraction process. The objective is to recover provitamin A (β-carotene) from palm oil (esterified) using carbon dioxide/ethanol as the supercritical mixed solvent. This example characterizes the problem for recovering high added value product from natural sources, as the palm oil, which is desired by the market. Owing to the fact that esterified palm oil is a complex mixture, made by several components, in order to characterize this system in the simulator, it was necessary to create hypothetical components using the UNIFAC (universal function-group activity coefficients model) group contribution, because they are not present in a conventional database and, then, their physical properties must be estimated and/or predicted before the simulation. The optimization was carried out in each simulation for each equipment, in terms of operating conditions (temperature and pressure), in order to obtain the maximum recovery of carotenes. According to the results, it was possible to concentrate carotenes through two cycles of supercritical extraction with high yield. Furthermore, ethyl esters (biodiesel) were also obtained, as a byproduct of the proposed process, which can also be used as an alternative fuel, with the important characteristic that it is renewable.     

Flow injection analysis of fluoride: optimization of experimental conditions and non-linear calibration using artificial neural networks

This paper deals with the application of artificial neural networks (ANNs) to two common problems in spectroscopy: optimization of experimental conditions and non-linear calibration of the result, with particular reference to the determination of fluoride by flow injection analysis (FIA). The FIA system was based on the formation of a blue ternary complex between zirconium(IV), p-methyldibromoarsenazo and F- with the maximum absorption wavelength at 635 nm. First, optimization in terms of sensitivity and sampling rate was carried out by using jointly a central composite design and ANNs, and a neural network with a 3-7-1 structure was confirmed to be able to provide the maximum performance. Second, the relationship between the concentration of fluoride and its absorbance was modeled by ANNs. In this process, cross-validation and leave-k-out were used. The results showed that good prediction was attained in the 1-4-1 neural net. The trained networks proved to be very powerful in both applications. The proposed method was successfully applied to the determination of free fluoride in tea and toothpaste with recoveries between 96 and 101%.     

Maximum work configurations of finite potential capacity reservoir chemical engines

An isothermal endoreversible chemical engine operating between the finite potential capacity high-chemical-potential reservoir and the infinite potential capacity low-chemical-potential reservoir has been studied in this work.Optimal control theory was applied to determine the optimal cycle configurations corresponding to the maximum work output per cycle for the fixed total cycle time and a universal mass transfer law.Analyses of special examples showed that the optimal cycle configuration with the mass tr...     

有限势库化学机最大输出功时循环最优构型

基于有限势容高化学势库和无限势容低化学势库间工作的等温内可逆化学机,在循环总时间一定的条件下,应用最优控制理论导出了一类普适传质规律下对应于最大输出功时化学机循环最优构型.特例分析表明传质定律为g∝△μ(△μ为化学势差)时循环最优构型为低化学势侧工质中关键组分的化学势(或浓度)为常数,而工质与有限势容高化学势库间关键组分的化学势(或浓度)均随时间呈非线性变化且化学势之差(浓度之比)为常数的等温内可逆化学机循环;传质定律为g∝△c(△c为浓度差)时循环最优构型与传质定律g∝△μ下循环最优构型存在显著不同;当高化学势库也为无限势容化学势库时,循环最优构型由两个等化学势传质分支和两个等质量流率分支组成,且与具体的传质定律无关.本文的研究对象具有一定的普适性,研究结果对于实际化学机的优化设计与最优运行具有一定参考价值.     

From ergodicity to extended phase diagrams

Structure prediction of stable and metastable phases is put on equal footing for the first time, with a solid thermodynamical background. How to estimate the lifetime of metastable phases is demonstrated by recent groundbreaking work of Jansen, Pentin, and Sch?n. At the heart lies the exploration of the Gibbs free-energy landscapes and the extended phase diagrams for complex systems.     

Predicting and evaluating separation quality of micellar electrokinetic capillary chromatography by artificial neural networks

Computer-aided optimization of micellar electrokinetic capillary chromatography (MEKC) separations was demonstrated by artificial neural networks (ANNs) using a Levenberg-Marquardt algorithm and an orthogonal experimental design. A novel criterion, named Q, for evaluating the separation quality of MEKC was firstly presented, which considered both separation selectivity and analysis time. MEKC separation conditions of seven plant hormones were then simulated and optimized using ANNs based on this novel criterion. The result was further compared to that obtained using ANNs based on a traditionally used criterion of overall normalization resolution (named r). Finally, the separation under optimum conditions predicted by ANNs using the criterion Q was compared to, and proved to be better than that obtained by empirical step-by-step optimization procedures. This method may also be adapted to other separation methods due to its generality.     

Parametric analysis of the inductively coupled plasma

A systematic parametric study of an LTE (local thermodynamic equilibrium) mathematical model of pure inductively coupled argon plasma (ICAP) used for spectrochemical purposes was performed by means of computer simulations. The spatial distributions of temperature, gas velocity, magnetic field and energy losses were investigated under typical plasma operating conditions as function of the ICAP geometrical dimensions and dynamic parameters. These theoretical calculations can be used to predict the properties of the applied plasma in the course of practical work, to choose the optimal conditions by changing the operating parameters and to interpret existing analytical results.     

Porous Nitrogen‐doped Reduced Graphene Oxide Gels as Efficient Supercapacitor Electrodes and Oxygen Reduction Reaction Electrocatalysts

Heteroatom‐doped carbon materials have been widely used in energy storage and conversion such as supercapacitors and electrocatalysts. In this work, L‐asparagine (Asn), an amino acid derivative, has been used as a doping agent to prepare nitrogen‐ doped reduced graphene oxide gels (N‐GAs). The 3D interconnected structure gives rise to the superior electrochemical properties for supercapacitor and electrocatalytic oxygen reduction reaction (ORR). The N‐GA‐4 (the mass ratio of Asn to graphene oxide (GO) is 4 : 1 by hydrothermal method) electrode shows the capacitance of 291.6 F·g^–1 at 0.5 A·g^–1. Meanwhile, the assembled symmetric supercapacitor achieves a maximum energy density of 23.8 Wh· kg^–1 when the power density is 451.2 W·kg^–1, and demonstrates an ultralong cycling life that the retention of capacitance is 99.3% after 80000 cycles. What's more, the annealed aerogel N‐GA‐4‐900 exhibits an onset potential (E_onset) of 0.95 V, half wave potential (E_1/2) of 0.84 V (vs. RHE) and the oxygen reduction current density of 5.5 mA·cm^–2 at 0.1 V with nearly four‐electron transfer, which are superior to commercial Pt/C. This work offers a new insight into the synthesis and applications of N‐GAs materials towards high performance in supercapacitors and ORR.     

Analysis of volatile components of drugs and explosives by solid phase microextraction-ion mobility spectrometry

Current ion mobility spectrometry (IMS) devices are used to detect drugs and explosives in the form of particles and, in cases where the vapor pressure of the drugs or explosives is sufficiently high, the gas can be sampled and detected directly. The aim of this study is to demonstrate the use of solid phase microextraction (SPME) as a preconcentration technique coupled to an IMS for the detection of odor signature compounds of drugs and explosives. The reduced mobilities (K(o)) and IMS operating conditions for the odor signature compounds of cocaine, marijuana, and 3,4-methylenedioxy-N-methylamphetamine (MDMA) are reported for the first time. LODs, linear dynamic ranges (LDRs), and the precision of the analysis of these odor signature compounds, and the explosive taggant 2,3-dimethyl-2,3-dinitrobutane (DMNB) were obtained by SPME-IMS and normal IMS conditions. The systematic optimization of the IMS operating parameters for the detection of these odor compounds is also reported incorporating the use of genetic algorithms (GAs) for finding the optimal settings for the detection of these compounds of interest. These results support the case for targeting volatile components as a presumptive detection for the presence of the parent compounds of drugs and explosives. Furthermore, the IMS-specific GA developed can be used as an optimization tool for the detection of other compounds of interest in future work.     

A novel quantitative model of cell cycle progression based on cyclin-dependent kinases activity and population balances

Cell cycle regulates proliferative cell capacity under normal or pathologic conditions, and in general it governs all in vivo/in vitro cell growth and proliferation processes. Mathematical simulation by means of reliable and predictive models represents an important tool to interpret experiment results, to facilitate the definition of the optimal operating conditions for in vitro cultivation, or to predict the effect of a specific drug in normal/pathologic mammalian cells. Along these lines, a novel model of cell cycle progression is proposed in this work. Specifically, it is based on a population balance (PB) approach that allows one to quantitatively describe cell cycle progression through the different phases experienced by each cell of the entire population during its own life. The transition between two consecutive cell cycle phases is simulated by taking advantage of the biochemical kinetic model developed by Gérard and Goldbeter (2009) which involves cyclin-dependent kinases (CDKs) whose regulation is achieved through a variety of mechanisms that include association with cyclins and protein inhibitors, phosphorylation–dephosphorylation, and cyclin synthesis or degradation. This biochemical model properly describes the entire cell cycle of mammalian cells by maintaining a sufficient level of detail useful to identify check point for transition and to estimate phase duration required by PB. Specific examples are discussed to illustrate the ability of the proposed model to simulate the effect of drugs for in vitro trials of interest in oncology, regenerative medicine and tissue engineering.     

Ultrafiltration of BSA in pulsating conditions: an artificial neural networks approach

The aim of the present paper is to analyze membrane systems behavior, operating in pulsating conditions, by means of artificial neural networks (ANNs). Different ANNs have been developed, by means of Matlab^® Neural Network Toolbox, to model the ultrafiltration process of aqueous BSA solutions through poly-ethersulfone membranes. A specific neural network architecture, constituted by one input layer, two hidden layers and one output layer, has been finally identified by a trial-and-error procedure. The network has been trained through a selected set of experimental data obtained for a lab-scale flat sheet membrane module, equipped with a device capable of producing periodic pulses of the applied trans-membrane pressure (TMP) and feed flow rate. It has been found that the developed neural network is capable of offering very accurate predictions of actual system behavior either when it is tested within the range used for training or when the inputs combination has been never exploited during learning phase. The observed reliability of neural networks predictions of membrane performances has suggested to use them for searching an optimal pulsation frequency profile able to maximize permeate flux. The utilization of such a pulse frequency profile allows obtaining, on the basis of theoretical evaluations only, significant improvements of membrane performances with respect to UF experiments performed at fixed and constant pulsation frequencies.