Recent Progress on Microfine Design of Metal–Organic Frameworks: Structure Regulation and Gas Sorption and Separation

Metal–organic frameworks (MOFs) have emerged as an important and unique class of functional crystalline hybrid porous materials in the past two decades. Due to their modular structures and adjustable pore system, such distinctive materials have exhibited remarkable prospects in key applications pertaining to adsorption such as gas storage, gas and liquid separations, and trace impurity removal. Evidently, gaining a better understanding of the structure–property relationship offers great potential for the enhancement of a given associated MOF property either by structural adjustments via isoreticular chemistry or by the design and construction of new MOF structures via the practice of reticular chemistry. Correspondingly, the application of isoreticular chemistry paves the way for the microfine design and structure regulation of presented MOFs. Explicitly, the microfine tuning is mainly based on known MOF platforms, focusing on the modification and/or functionalization of a precise part of the MOF structure or pore system, thus providing an effective approach to produce richer pore systems with enhanced performances from a limited number of MOF platforms. Here, the latest progress in this field is highlighted by emphasizing the differences and connections between various methods. Finally, the challenges together with prospects are also discussed.     

Efficient prediction of drug–drug interaction using deep learning models

A drug–drug interaction or drug synergy is extensively utilised for cancer treatment. However, prediction of drug–drug interaction is defined as an ill‐posed problem, because manual testing is only implementable on small group of drugs. Predicting the drug–drug interaction score has been a popular research topic recently. Recently many machine learning models have proposed in the literature to predict the drug–drug interaction score efficiently. However, these models suffer from the over‐fitting issue. Therefore, these models are not so‐effective for predicting the drug–drug interaction score. In this work, an integrated convolutional mixture density recurrent neural network is proposed and implemented. The proposed model integrates convolutional neural networks, recurrent neural networks and mixture density networks. Extensive comparative analysis reveals that the proposed model significantly outperforms the competitive models.Inspec keywords: cancer, learning (artificial intelligence), drugs, recurrent neural nets, convolutional neural nets, drug delivery systemsOther keywords: drug synergy, drug–drug interaction score, drug–drug interaction prediction, deep learning, cancer treatment, machine learning, convolutional mixture density recurrent neural network     

Photosensitization of TiO2 Nanostructures with CdS Quantum Dots: Particulate versus Tubular Support Architectures

TiO₂ nanotube arrays and particulate films are modified with CdS quantum dots with an aim to tune the response of the photoelectrochemical cell in the visible region. The method of successive ionic layer adsorption and reaction facilitates size control of CdS quantum dots. These CdS nanocrystals, upon excitation with visible light, inject electrons into the TiO₂ nanotubes and particles and thus enable their use as photosensitive electrodes. Maximum incident photon to charge carrier efficiency (IPCE) values of 55% and 26% are observed for CdS sensitized TiO₂ nanotube and nanoparticulate architectures respectively. The nearly doubling of IPCE observed with the TiO₂ nanotube architecture is attributed to the increased efficiency of charge separation and transport of electrons.     

Synergistic Use of Pyridine and Selenophene in a Diketopyrrolopyrrole‐Based Conjugated Polymer Enhances the Electron Mobility in Organic Transistors

To achieve semiconducting materials with high electron mobility in organic field‐effect transistors (OFETs), low‐lying energy levels (the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO)) and favorable molecular packing and ordering are two crucial factors. Here, it is reported that the incorporation of pyridine and selenophene into the backbone of a diketopyrrolopyrrole (DPP)‐based copolymer produces a high‐electron‐mobility semiconductor, PDPPy‐Se. Compared with analogous polymers based on other DPP derivatives and selenophene, PDPPy‐Se features a lower LUMO that can decrease the electron transfer barrier for more effective electron injection, and simultaneously a lower HOMO that, however, can increase the hole transfer barrier to suppress the hole injection. Combined with thermal annealing at 240 °C for thin film morphology optimization to achieve large‐scale crystallite domains with tight molecular packing for effective charge transport along the conducting channel, OFET devices fabricated with PDPPy‐Se exhibit an n‐type‐dominant performance with an electron mobility (μ_e) as high as 2.22 cm² V^?1 s^?1 and a hole/electron mobility ratio (μ_h/μ_e) of 0.26. Overall, this study demonstrates a simple yet effective approach to boost the electron mobility in organic transistors by synergistic use of pyridine and selenophene in the backbone of a DPP‐based copolymer.     

On the behavior of thin walled composite beams with stochastic properties under matrix cracking damage

Thin walled composite beam structures are prone to damage which results in change in the performance of these structures. The change in the performance due to damage may get confused with the variation in the performance due uncertainties in the properties of these structures. Here, the performances of the thin walled composite beam under matrix cracking damage having material uncertainties are studied. The cross-sectional stiffness properties are obtained using thin walled beam formulation, which is based on a mixed force and displacement method. The stochastic behaviors of material properties are obtained from previous experimental and analytical studies. The effects of matrix cracking are introduced through the changes in the extension, extension–bending and bending matrices of composites. The effects of matrix cracking on out-of-plane bending, inplane bending and torsion cross-sectional properties are studied at different crack densities for stochastic material properties. Further, the effects of matrix cracking and uncertainties on measurable properties such as deflections and frequencies are studied. Results show that the beam responses at different crack densities get mixed due to the material uncertainties. The estimates of variance obtained for observable system properties due to uncertainty can be used for developing more robust damage detection algorithms.     

Classification of drug molecules for oxidative stress signalling pathway

In humans, oxidative stress is involved in the development of diabetes, cancer, hypertension, Alzheimers’ disease, and heart failure. One of the mechanisms in the cellular defence against oxidative stress is the activation of the Nrf2‐antioxidant response element (ARE) signalling pathway. Computation of activity, efficacy, and potency score of ARE signalling pathway and to propose a multi‐level prediction scheme for the same is the main aim of the study as it contributes in a big amount to the improvement of oxidative stress in humans. Applying the process of knowledge discovery from data, required knowledge is gathered and then machine learning techniques are applied to propose a multi‐level scheme. The validation of the proposed scheme is done using the K‐fold cross‐validation method and an accuracy of 90% is achieved for prediction of activity score for ARE molecules which determine their power to refine oxidative stress.Inspec keywords: cancer, cellular biophysics, biochemistry, drugs, molecular biophysics, proteins, learning (artificial intelligence), medical computingOther keywords: oxidative stress, Nrf2‐antioxidant response element signalling pathway, ARE signalling pathway, diabetes, cancer, hypertension, Alzheimers’ disease, heart failure, machine learning techniques, K‐fold cross‐validation method, ARE molecules     

Data input model for virtual reality-aided facility layout

An approach to automatically extract three dimensional (3D) models (that is, geometries and topologies) of physical objects in a facility is described. The rationale for this work is its repeated use in efficiently developing databases of 3D objects for applying virtual reality (VR) tools in detailed layout decision support. Obtaining 3D object models can be a challenging task. Sometimes they are available, for example, in a Computer-Aided Design (CAD) database and these can be readily imported into a VR database. But on many occasions one is not so fortunate and these object models have to be created in correlation to an existing or proposed facility, which can be an extremely tedious and time consuming task. A time efficient and economical alternative is to use video camera images, but quickly and accurately capturing the depth information from 2D camera images has so far remained elusive because the existing methodologies are too general purpose and operate at a lower level of abstraction, namely digitized images. We have developed a method for directly inputting 3D objects into VR-aided facility layout models, by integrating the strengths of previously tried and tested technological components: (i) camera calibration; (ii) image processing; (iii) stereo vision; and (iv) Delaunay triangulation. The techniques described here are embedded in a prototype architecture and toolkit called MIRRORS (Methodology for Inputting Raw Recordings into 3D Object Renderings for Stereo). The primary contribution of this paper is that it has been able to design an integrated system to build 3D object models from 2D images. The MIRRORS system has been primarily designed for objects without free-form surfaces and whose shape can be recovered from a relatively nondense set of points.     

Surface Oxidation as a Cause of High Open‐Circuit Voltage in CdSe ETA Solar Cells

TiO₂/CdSe/CuSCN extremely thin absorber (ETA) solar cells are found to give relatively high values of open‐circuit voltage (>0.8 V) but low currents upon annealing the cadmium selenide (CdSe) in air (500 ºC). Annealing in N₂ produces much lower photovoltages and slightly lower photocurrents. Band structure measurements show differences between the two annealing regimes that, however, appear to favor the N₂‐annealed CdSe. On the other hand, chemically resolved electrical measurements (CREM) of the cells reveal marked differences in photo‐induced charge trapping, in particular at absorber grain boundaries of the air versus N₂‐annealed systems, correlated with the formation of Cd–O species at the CdSe surface. Using transient absorption and photovoltage decay, pronounced lifetime differences are also observed, in agreement with the strong suppression of charge recombination. The results point to a multiple role of grain surface‐oxidation, which both impedes electron injection from the CdSe to the TiO_2, but, much more significantly, enhances hole injection to the CuSCN via passivation of hole traps that act as efficient recombination centers.     

Evaluation of numerical schemes using different simulation methods for the continuous phase modeling of cyclone separators

The steady and unsteady state simulations of Stairmand cyclone separator were carried out to investigate the performance of different interpolation schemes for discretization of pressure gradient and advection terms. The RSM turbulence model was revisited to explore its simulation capability of PVC phenomenon and fluctuating velocity profiles of cyclone separators. The combination of Presto, SO, standard and BFW schemes for discretization of pressure gradient and FOU, power law, SOU, QUICK and MUSCL schemes for discretization of advection terms were studied. The double precision solver of Fluent 6.3.26 and modified RSM turbulence model constants of Jiao et al. (Chem. Eng. Technol. 30 (2007) 15–20) were also verified for simulation of cyclone separators. The predicted mean and fluctuating velocity profiles and pressure drop inside the cyclone separator with steady and unsteady simulations have been compared to experimental results available in literature.The steady state simulation failed to predict velocity profiles and pressure drop inside cyclone separator accurately, whereas the unsteady state simulation predicted velocity profiles, pressure drop and PVC phenomenon close to experimental values. The prediction of fluctuating velocity profile was better than previously reported work in the core region compared to the off core region. The present study revealed that the SOU scheme for discretization of advection terms of momentum, kinetic energy and its dissipation rate equations and the FOU scheme for Reynolds stresses together with the Presto scheme for discretization of pressure gradient with unsteady simulation are the optimum choice for simulation of cyclone separators.