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An overview of alcoholic beverages discrimination plus the author’s experimental research work on identification of bland Chinese liquors is presented in this article. First, a summary was given on...

It is difficult to avoid the formation of polysulfides by traditional chemical methods, and the synthesis of high purity amorphous MoS2 nanomaterials under ambient conditions is still a challenging task. Here we present a new and facile photochemical strategy for the synthesis of amorphous MoS2 nanomaterials, which is achieved by irradiating a mixed solution containing ammonium molybdate, formic acid and sodium sulfide simply with a Xe lamp for 3 min. The mechanism study reveals that the key step in this synthesis is the photolysis of formic acid to produce free radicals which can rapidly reduce Mo6+ to Mo4+, which then combines with S2− to form MoS2 and inhibits the formation of S–S2− by preventing S2− from participating in the reduction reaction. In addition, the results of a series of experiments indicate that the as-prepared amorphous MoS2 features a small particle size, uniform morphology and relatively large specific surface area, and shows excellent performance in the removal of inorganic heavy metal ions (mercury, lead and cadmium ions) and organic pollutants (rhodamine B and tetracycline), catalase catalysis and a lithium battery anode, showing its great potential and broad application prospects in the fields of environmental remediation, clean energy and green catalysis.

The aim of this study was to illustrate the applicability of chemometric intraregional discrimination of three famous Chinese liquors in Sichuan province of China based on multi-element analytical ...

A disposable, instrument-free, height readout paper-based analytical device (HR-PAD) based on a paper strip inkjet-printed with CdTe quantum dots (QDs) was developed for the sensitive speciation analysis of Ag+ and silver nanoparticles (AgNPs). When the paper strip is immersed into a sample solution, capillary action draws it through the surface and any Ag+ in the solution quenches the fluorescence of CdTe QDs via a cation exchange reaction between Ag+ and the CdTe QDs, with the height of the quenched band being proportional to the concentration of Ag+. In contrast, fluorescence quenching cannot be observed when only AgNPs are present in solution. Thus, the concentration of AgNPs can be obtained by subtracting the Ag+ content from the total silver determined by the HR-PAD after digestion with HNO3. Under optimized conditions, the methodology provides high selectivity, sensitivity, and accuracy for the detection of Ag+ or AgNPs in various samples, even at concentrations as low as 0.05 mg L-1. Precisions of 4.5% and 2.2% RSDs were achieved at concentrations of 1 mg L-1 and 7 mg L-1 of Ag+, respectively. Compared to conventional methods, this approach is inexpensive and user-friendly and eliminates the need for expensive and sophisticated detection instruments. The practicality of the method was demonstrated via the speciation analysis of AgNPs and Ag+ in river water and 12 commercial products with satisfactory results.

It is difficult to avoid the formation of polysulfides by traditional chemical methods, and the synthesis of high purity amorphous MoS

We describe a novel framework for estimating subsurface properties, such as rock permeability and porosity, from time-lapse observed seismic data by coupling full-waveform inversion, subsurface flow processes, and rock physics models. For the inverse modeling, we handle the back-propagation of gradients by an intrusive automatic differentiation strategy that offers three levels of user control: (1) at the wave physics level, we adopted the discrete adjoint method in order to use our existing high-performance FWI code; (2) at the rock physics level, we used built-in operators from the $\texttt{TensorFlow}$ backend; (3) at the flow physics level, we implemented customized PDE operators for the potential and nonlinear saturation equations. These three levels of gradient computation strike a good balance between computational efficiency and programming efficiency, and when chained together, constitute a coupled inverse system. We use numerical experiments to demonstrate that (1) the three-level coupled inverse problem is superior in terms of accuracy to a traditional decoupled inversion strategy; (2) it is able to simultaneously invert for parameters in empirical relationships such as the rock physics models; and (3) the inverted model can be used for reservoir performance prediction and reservoir management/optimization purposes.

Artificial neural network (ANN) models are used to learn the nonlinear constitutive laws based on indirectly measurable data. The real input and output of the ANN model are derived from indirect data using a mechanical system, which is composed of several subsystems including the ANN model. As the ANN model is coupled with other subsystems, the input of the ANN model needs to be determined during the training. This approach integrates measurable data, mechanics, and ANN models so that the ANN models can be trained without direct data which is usually not available from experiments. Two examples are provided as an illustration of the proposed approach. The first example uses two-dimensional (2D) finite element (FE) analysis to train an ANN model to learn the nonlinear in-plane shear constitutive law. The second example applies a continuum damage model to train an ANN model to learn the damage accumulation law. The results show that the trained ANN models achieve great accuracy based on the proposed approach.

Full-waveform inversion (FWI) is an accurate imaging approach for modeling the velocity structure by minimizing the misfit between recorded and predicted seismic waveforms. However, the strong nonlinearity of FWI resulting from fitting oscillatory waveforms can trap the optimization in local minima. We have adopted a neural-network-based full-waveform inversion (NNFWI) method that integrates deep neural networks with FWI by representing the velocity model with a generative neural network. Neural networks can naturally introduce spatial correlations as regularization to the generated velocity model, which suppresses noise in the gradients and mitigates local minima. The velocity model generated by neural networks is input to the same partial differential equation (PDE) solvers used in conventional FWI. The gradients of the neural networks and PDEs are calculated using automatic differentiation, which back propagates gradients through the acoustic PDEs and neural network layers to update the weights of the generative neural network. Experiments on 1D velocity models, the Marmousi model, and the 2004 BP model determine that NNFWI can mitigate local minima, especially for imaging high-contrast features such as salt bodies, and it significantly improves the inversion in the presence of noise. Adding dropout layers to the neural network model also allows analyzing the uncertainty of the inversion results through Monte Carlo dropout. NNFWI opens a new pathway to combine deep learning and FWI for exploiting the characteristics of deep neural networks and the high accuracy of PDE solvers. Because NNFWI does not require extra training data and optimization loops, it provides an attractive and straightforward alternative to conventional FWI.

A highly selective, sensitive yet simple colorimetric assay was developed for Se(IV) detection based on the silver nanoparticles (AgNPs), with hydride generation for separation and sample introduction. The proposed method could be easily performed by introducing the hydride vapor of the analyte (H2Se) into a thioglycolic acid (TGA) capped AgNPs solution. A combination of experimental and theoretical studies was used to illustrate the mechanism of this AgNPs colorimetric assay. The strong interaction of AgNPs with Se0 (oxidation product of H2Se) and/or H2Se weakens the stabilizing effect of TGA on AgNPs, leading to the aggregation of AgNPs and thus the color change of the AgNPs solution from bright yellow to light yellow or even colorless with the increase of Se(IV) concentration. The limit of detection was found to be 5 μg L−1 with UV–vis spectrophotometric detection, and 50 μg L−1 with the naked eye visual observation. The proposed method showed excellent selectivity and was successfully applied for Se(IV) determination in fish protein and sea water samples.

Cataluminescence (CTL) is one of the most important sensing-transduction principles for the real-time monitoring of atmospheric pollutants. Highly sensitive CTL-based CO detection still remains a challenge because of the relatively poor reactivity of CO and the low catalytic efficiency of the catalysts. Herein, combining ultraviolet (UV)-light activation and chemical modification of the sensing element, we have successfully established a UV-assisted CTL sensor for gaseous CO based on g-C3N4 with high sensitivity, selectivity, and stability. UV irradiation can efficiently activate CO molecules and induce the generation of reactive oxygen species (ROS) for CO oxidation. Furthermore, carboxyl groups greatly facilitate the chemisorption of CO on functionalized g-C3N4 nanomaterials, thus enhancing the CTL sensitivity. The influences of experimental conditions and the possible catalytic mechanism of CO on functionalized g-C3N4 have been investigated in detail. Under the optimal experimental conditions, the proposed CTL sensor presents a detection limit (3σ) toward CO of 0.008 μg mL-1, which is much lower than the maximum allowable emission concentration of CO in atmospheric conditions (0.030 μg mL-1). The UV-CTL system is green, sensitive, stable, and low cost, and thus it possesses great potential application in gas sensing.

Overexpression and crystallization of uric acid have been recognized as the course of hyperuricemia and gout, which is produced via xanthine oxidase (XOD)-catalyzed oxidation of xanthine. Therefore, the medicinal therapy of hyperuricemia and gout is majorly based on the inhibition of the XOD enzymatic pathway. The spectroscopic nature of xanthine and uric acid, namely both absorption (near the ultraviolet region) and emission (non-fluorescent) characteristics, hinders optical assay development for XOD analysis. Therefore, the state-of-the-art analysis of XOD and the screening of XOD inhibitors are majorly based on chromatography. Here, we found the near ultraviolet absorption of uric acid overlapped well with the absorption of a large bandgap semiconductor quantum dots, ZnS. On the other hand, the intrinsic weak fluorescence of ZnS QDs can be substantially improved via transition metal ion doping. Therefore, herein, we developed an inner filter effect-based assay for XOD analysis and inhibitor screening with Mn-doped ZnS QDs. The phosphorescence of Mn-doped ZnS QDs could be quenched by uric acid generated from xanthine catabolism by XOD, leading to the phosphorescence turn-off detection of XOD with a limit of detection (3σ) of 0.02 U L-1. Furthermore, the existence of XOD inhibitors could inhibit the XOD enzymatic reaction, resulting in weakened phosphorescence quenching. Therefore, the proposed assay could also be explored for the facile screening analysis of XOD inhibitors, which is important for the potential medicinal therapy of hyperuricemia and gout.

We propose a novel approach to model viscoelasticity materials, where rate-dependent and non-linear constitutive relationships are approximated with deep neural networks . We assume that inputs and outputs of the neural networks are not directly observable, and therefore common training techniques with input–output pairs for the neural networks are inapplicable. To that end, we develop a novel computational approach to both calibrate parametric and learn neural-network-based constitutive relations of viscoelasticity materials from indirect displacement data in the context of multiple-physics systems. We show that limited displacement data holds sufficient information to quantify the viscoelasticity behavior. We formulate the inverse computation – modeling viscoelasticity properties from observed displacement data – as a PDE-constrained optimization problem and minimize the error functional using a gradient-based optimization method. The gradients are computed by a combination of automatic differentiation and implicit function differentiation rules. The effectiveness of our method is demonstrated through numerous benchmark problems in geomechanics and porous media transport.

Inverse problems associated with stochastic models constitute a significant portion of scientific and engineering applications. In such cases the unknown quantities are distributions. The applicability of traditional methods is limited because of their demanding assumptions or prohibitive computational consumption; for example, maximum likelihood methods require closed-form density functions, and Markov Chain Monte Carlo needs a large number of simulations. We propose a new method that estimates the unknown distribution by matching the statistical properties between observed and simulated random processes. We leverage the expressive power of neural networks to approximate the unknown distribution and use a discriminative neural network for computing the statistical discrepancies between the observed and simulated random processes. We demonstrated numerically that the proposed methods can estimate both the model parameters and learn complicated unknown distributions.

An environmentally friendly, low power consuming, sensitive and compact mercury analyzer was developed for the determination of mercury in water samples by integrating a thin film dielectric barrier discharge induced cold vapor reactor and a dielectric barrier discharge optical emission spectrometer into a small polymethyl methacrylate plate (10.5 cm length × 8.0 cm width × 1.2 cm height). Mercury cold vapor was generated when standard or sample solutions with or without formic acid were introduced to the reactor to form thin film liquid and exposed to microplasma irradiation and subsequently separated from the liquid phase for transport to the microplasma and detection of its atomic emission. Limits of detection of 0.20 μg L −1 and 2.6 μg L −1 were obtained for the proposed system using or not using formic acid, respectively. Compared to the conventional microplasma optical emission spectrometry used for mercury analysis, this system not only retains the good limit of detection amenable to the determination of mercury in real samples, but also reduces power consumption, eliminates the generation of hydrogen and avoids the use of toxic or unstable reductant. Method validation was demonstrated by analysis of a certified reference material of water sample and three real water samples with good spike recoveries (88–102%).

We present a new approach for predictive modeling and its uncertainty quantification for mechanical systems, where coarse-grained models such as constitutive relations are derived directly from observation data. We explore the use of a neural network to represent the unknown constitutive relations, compare the neural networks with piecewise linear functions, radial basis functions, and radial basis function networks, and show that the neural network outperforms the others in certain cases. We analyze the approximation error of the neural networks using a scaling argument. The training and predicting processes in our framework combine the finite element method, automatic differentiation, and neural networks (or other function approximators). Our framework also allows uncertainty quantification in the form of confidence intervals. Numerical examples on a multiscale fiber-reinforced plate problem and a nonlinear rubbery membrane problem from solid mechanics demonstrate the effectiveness of our framework., 40 pages, 21 figures

The excellent fluorescence property of Au nanoclusters (AuNCs) has received great attention for various chemosensing and biorelated applications, but the sample matrix is still an important problem that causes undesirable fluorescence variation. On the one hand, hydride generation (HG) is an effective strategy to separate the target analyte from the complex sample matrices, but the implementation of HG with AuNC-based fluorescent assays was not realized. On the other hand, due to the ultrasmall size of AuNCs and good catalytic performance of Au, AuNCs are also featuring intriguing catalytic applications. Herein, we proposed a new type of AuNC-based fluorescence assay for Se(IV) detection, in which hydride generation of Se(IV) was coupled with the fluorescence/catalytic dual functions of AuNCs. In a batch hydride generation mode, Se(IV) was first converted to volatile H2Se. When it spread in the headspace to contact with AuNCs supported paper, AuNC-catalyzed oxidation of H2Se by O2 to yield elemental selenium occurred, which further deposited on the surface of AuNCs to induce fluorescence quenching. The catalytic effect of AuNCs was studied in depth via both experimental and theoretical (density functional theory) investigations. Three main steps for H2Se oxidation were identified, with energy barriers in the presence of AuNCs significantly lower than those without. Benefiting from the reduced matrix interference by hydride generation and the unique catalysis/fluorescence of AuNCs, the proposed assay featured high selectivity, good sensitivity, and simplicity, with successful applications for selenium detection in real samples.

Imaging Earth structure or seismic sources from seismic data involves minimizing a target misfit function, and is commonly solved through gradient-based optimization. The adjoint-state method has been developed to compute the gradient efficiently; however, its implementation can be time-consuming and difficult. We develop a general seismic inversion framework to calculate gradients using reverse-mode automatic differentiation. The central idea is that adjoint-state methods and reverse-mode automatic differentiation are mathematically equivalent. The mapping between numerical PDE simulation and deep learning allows us to build a seismic inverse modeling library, ADSeismic, based on deep learning frameworks, which supports high performance reverse-mode automatic differentiation on CPUs and GPUs. We demonstrate the performance of ADSeismic on inverse problems related to velocity model estimation, rupture imaging, earthquake location, and source time function retrieval. ADSeismic has the potential to solve a wide variety of inverse modeling applications within a unified framework.

A simple, non-chromatographic and green method based on flow injection UV photochemical or ultrasonic vapor generation atomic fluorescence spectrometry (AFS) was developed for the determination and speciation analysis of mercury. Mercury cold vapor (Hg0) was generated by using only formic acid and UV or ultrasonic irradiation, and was subsequently detected by AFS. Both mercury (Hg2 +) and methyl mercury (MeHg) can be converted to Hg0 for the determination of total mercury with UV irradiation, while only Hg2 + can be reduced to Hg0 with ultrasonic irradiation, thus determining only Hg2 +. Then, the concentration of MeHg can be calculated by subtracting the Hg2 + concentration from the total mercury concentration. The optimal conditions for the best cold vapor generation efficiencies are discussed in detail, together with interference from transition metals. This new speciation analysis not only provides high sensitivity for the determination of mercury species but further eliminates the use of toxic reducing reagents and avoids potential destruction of analyte species that occur in chromatographic separation. Moreover, a simpler and less toxic Hg2 + standard series can be used for the calibration of both Hg2 + and MeHg. The limit of detection is 0.005 or 0.01 μg L− 1 for total mercury with the UV or inorganic mercury with the ultrasonic irradiation, respectively. This method was successfully applied to ultrasensitive mercury speciation analysis of water and biological samples.

A new digestion method using UV-assisted Fe(0) Fenton reaction was developed for the determination of trace Cd in rice by hydride generation atomic fluorescence spectrometry. The proposed method integrated the advantages of simplicity, small dose of reagents, low cost and moderate reaction conditions, and was successfully utilized to analyze a Certified Reference Material (CRM) and real rice samples. A 1 mL mixture of the sample and reagents (0.0500 g rice powder, 0.2% (m/v) Fe(0), 0.75% (v/v) HNO3 and 18% (v/v) H2O2) was irradiated by UV-light for 50 min and then a clear solution was obtained by separating excess Fe(0) with a magnet prior to spectral analysis. The limit of detection (LOD) for Cd was found to be 0.02 mg kg(-1) and the relative standard deviation was better than 5.0% at a concentration level of 0.40 mg kg(-1). The recovery obtained by analyzing the CRM was 103% and spiked recoveries with 0.40 mg kg(-1) Cd in rice samples were 93% and 101%. The t-test proved that there is no significant difference between the certified value and the determined value of the CRM, and between the proposed method and microwave-assisted digestion coupled with inductively coupled plasma mass spectrometry (MWD-ICP-MS) at 95% confidence level.

We consider the isogeometric analysis for fractional PDEs involving the fractional Laplacian in two dimensions. An isogeometric collocation method is developed to discretize the fractional Laplacian and applied to the fractional Poisson problem and the time-dependent fractional porous media equation. Numerical studies exhibit monotonous convergence with a rate of $\mathcal{O}(N^{-1})$, where $N$ is the degrees of freedom. A comparison with finite element analysis shows that the method enjoys higher accuracy per degree of freedom and has a better convergence rate. We demonstrate that isogeometric analysis offers a novel and promising computational tool for nonlocal problems., 23 pages, 8 figures

Cataluminescence (CTL) is one of the most important sensing-transduction principles for the real-time monitoring of atmospheric pollutants. Highly sensitive CTL-based CO detection still remains a challenge because of the relatively poor reactivity of CO and the low catalytic efficiency of the catalysts. Herein, combining ultraviolet (UV)-light activation and chemical modification of the sensing element, we have successfully established a UV-assisted CTL sensor for gaseous CO based on g-C

Cadmium ion-enhanced photochemical vapour generation directly from Se(vi) has for the first time been reported here, and the key mechanism of the in situ formation of nano-CdSe as a photocatalyst has been revealed.

In this work, we proposed a miniaturized point discharge-radical optical emission spectrometer (PD-RES) as a multichannel optical detector for discriminant analysis of various volatile organic sulfur compounds (VOSCs). Under appropriate experimental conditions, the unique molecular emission of CS radical in the vicinity of 257.6 nm was recorded, as well as the atomic emission lines of C at 193.1 nm and 247.8 nm, the molecular emission of C2 radical around 231.5 nm and CN radical nearby 384.8 nm. They were utilized as five optical channels for precise qualification and discrimination. Linear discriminant analysis (LDA) and principal component analysis (PCA) further demonstrated the robustness of this detector for discriminant analysis: 95 unknown samples from ten typical VOSCs were classified with accuracy of 98.9%. This proposed detector was further successfully applied to the discrimination of different concentrations of CS2 in air samples and two types of isomers (functional group isomer and carbon-chain isomer).

The adsorption of acetone on the pure and V-, Cr-, Mn-, Fe-, Co-, Ni-doped (TiO2)38 clusters was studied by the density functional theory (DFT) with the generalized gradient approximation (GGA) developed by the Perdew and Wang (PW91) exchange-correlation functional. The structural studies indicate that acetone adsorption prefers to take place on the top site with the symmetry plane of acetone perpendicular to the bridge oxygen rows. The properties of transition metal doped clusters were also been discussed. The calculated substitution energies show that the 3d transition metal doping is getting harder with the increase of atomic number of the doping atom. Furthermore, the adsorption of acetone on the doped clusters was investigated. The adsorption energies of Mn-, Co- and Ni-doped clusters are obviously larger than that of pure TiO2 cluster. The Mn, Co or Ni doping leads to acetone activation mainly on the C O group, but the Fe doping may reduce the activity.

A one-step, template-free method is described to synthesize porous carbons (PCs) in situ on a metal surface by using a room-temperature, atmospheric-pressure dielectric barrier discharge (DBD) plasma. This method not only features high efficiency, environmentally friendliness, and low cost and simple equipment, but also can conveniently realize large-area synthesis of PCs by only changing the design of the DBD reactor. The synthesized PCs have a regulated nestlike morphology, and thus, provide a high specific surface area and high pore volume, which result in excellent adsorption properties. Its applicability was demonstrated by using a PC-coated stainless-steel fiber as a solid-phase microextraction (SPME) fiber to preconcentrate polycyclic aromatic hydrocarbons (PAHs) prior to analysis by gas chromatography with flame ionization detection (GC-FID). The results showed that the fiber exhibited excellent enrichment factors (4.1×10(4) to 3.1×10(5)) toward all tested PAHs. Thus, the PC-based SPME-GC-FID provides low limits of detection (2 to 20 ng L(-1)), good precision (