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Durante casi un ano, la raza humana se ha visto sacudida por la aparicion de un nuevo coronavirus conocido como SARS-CoV-2, el cual se origino en China y desde alli se ha dispersado por casi todas ...

We develop an adaptive machine learning strategy in search of high-performance ABO3-type cubic perovskites for catalyzing the oxygen evolution reaction (OER). The strategy has two essential compone...

As the world enters its second year of the pandemic caused by SARS-CoV-2, intense efforts have been directed to develop an effective diagnosis, prevention, and treatment strategies. One promising drug target to design COVID-19 treatments is the SARS-CoV-2 M

Understanding the nature of chemical bonding and its variation in strength across physically tunable factors is important for the development of novel catalytic materials. One way to speed up this process is to employ machine learning (ML) algorithms with online data repositories curated from high-throughput experiments or quantum-chemical simulations. Despite the reasonable predictive performance of ML models for predicting reactivity properties of solid surfaces, the ever-growing complexity of modern algorithms, e.g., deep learning, makes them black boxes with little to no explanation. In this Perspective, we discuss recent advances of interpretable ML for opening up these black boxes from the standpoints of feature engineering, algorithm development, and post hoc analysis. We underline the pivotal role of interpretability as the foundation of next-generation ML algorithms and emerging AI platforms for driving discoveries across scientific disciplines. Accepted version

Despite recent advances of data acquisition and algorithms development, machine learning (ML) faces tremendous challenges to being adopted in practical catalyst design, largely due to its limited generalizability and poor explainability. Herein, we develop a theory-infused neural network (TinNet) approach that integrates deep learning algorithms with the well-established d-band theory of chemisorption for reactivity prediction of transition-metal surfaces. With simple adsorbates (e.g., *OH, *O, and *N) at active site ensembles as representative descriptor species, we demonstrate that the TinNet is on par with purely data-driven ML methods in prediction performance while being inherently interpretable. Incorporation of scientific knowledge of physical interactions into learning from data sheds further light on the nature of chemical bonding and opens up new avenues for ML discovery of novel motifs with desired catalytic properties., Machine learning faces challenges in catalyst design due to its black-box nature. Here, the authors develop a theory-infused neural network approach that integrates deep learning algorithms with the well-established d-band theory of chemisorption for reactivity prediction of transition-metal surfaces.

Objective Autism spectrum disorder (ASD) screening can improve prognosis via early diagnosis and intervention, but lack of time and training can deter pediatric screening. The Modified Checklist for Autism in Toddlers, Revised (M-CHAT-R) is a widely used screener but requires follow-up questions and error-prone human scoring and interpretation. We consider an automated machine learning (ML) method for overcoming barriers to ASD screening, specifically using the feedforward neural network (fNN). Methods The fNN technique was applied using archival M-CHAT-R data of 14,995 toddlers (age 16-30 months, 46.51% male). The 20 M-CHAT-R items were inputs, and ASD diagnosis after follow-up and diagnostic evaluation (i.e., ASD or not ASD) was the output. The sample was divided into subgroups by race (i.e., white and black), sex (i.e., boys and girls), and maternal education (i.e., below and above 15 years of education completed) to examine subgroup differences. Each subgroup was evaluated for best-performing fNN models. Results For the total sample, best results yielded 99.72% correct classification using 18 items. Best results yielded 99.92% correct classification using 14 items for white toddlers and 99.79% correct classification using 18 items for black toddlers. In boys, best results yielded 99.64% correct classification using 18 items, whereas best results yielded 99.95% correct classification using 18 items in girls. For the case when maternal education is 15 years or less (i.e., associate degree and below), best results were 99.75% correct classification when using 16 items. Results were essentially the same when maternal education was 16 years or more (i.e., above associate degree); that is, 99.70% correct classification was obtained using 16 items. Conclusion The ML method was comparable to the M-CHAT-R with follow-up items in accuracy of ASD diagnosis while using fewer items. Therefore, ML may be a beneficial tool in implementing automatic, efficient scoring that negates the need for labor-intensive follow-up and circumvents human error, providing an advantage over previous screening methods.

Owing to the remarkable advantages of the perovskite solar cell (PSC), people regard the PSC as an auspicious candidate for photovoltaic devices in the near future, but light instability of the PSC, caused by TiO2 being a photo catalyst to decompose perovskite films under UV radiation, limits its practical applications. To improve light stability, removing the TiO2 layer from PSC devices is the best solution, but the photovoltaic conversion efficiencies (PCEs) of the reported TiO2-layer-free PSCs are much lower than TiO2-layer-containing PSCs. As such, via applying a chemical engineering technique of inserting a layer of bathophenanthroline (Bphen) between the ITO substrate and the perovskite film, we developed a TiO2-layer-free PSC with both good light stability and an averaged PCE of around 18.53%, much higher than that of those reported ones. The insertion of the Bphen layer caused a dramatic promotion of shunt resistances (Rsh), causing the improvements of the open circuit voltages (Voc), fill factors...

Iontophoresis employs low-intensity electrical voltage and continuous constant current to direct a charged drug into a tissue. Iontophoretic drug delivery has recently been used as a novel method for cancer treatment in vivo. There is an urgent need to precisely model the low-intensity electric fields in cell culture systems to optimize iontophoretic drug delivery to tumors. Here, we present an iontophoresis-on-chip (IOC) platform to precisely quantify carboplatin drug delivery and its corresponding anti-cancer efficacy under various voltages and currents. In this study, we use an in vitro heparin-based hydrogel microfluidic device to model the movement of a charged drug across an extracellular matrix (ECM) and in MDA-MB231 triple-negative breast cancer (TNBC) cells. Transport of the drug through the hydrogel was modeled based on diffusion and electrophoresis of charged drug molecules in the direction of an oppositely charged electrode. The drug concentration in the tumor extracellular matrix was computed using finite element modeling of transient drug transport in the heparin-based hydrogel. The model predictions were then validated using the IOC platform by comparing the predicted concentration of a fluorescent cationic dye (Alexa Fluor 594 (R)) to the actual concentration in the microfluidic device. Alexa Fluor 594 (R) was used because it has a molecular weight close to paclitaxel, the gold standard drug for treating TNBC, and carboplatin. Our results demonstrated that a 50 mV DC electric field and a 3 mA electrical current significantly increased drug delivery and tumor cell death by 48.12% +/- 14.33 and 39.13% +/- 12.86, respectively (n = 3, p-value

Eukaryotic cells have a protective plasma membrane, which restricts the free movement of molecules from the external environment to the internal environment. This study aims to computationally model the transport of fluorescein derivatives across the monolayer of human brain microvascular endothelial cells (HBMEC). The determination of plausible effective diffusion constants (Deff) will allow models to be built that could be useful beyond in vitro experimentation. Fluorescein-5-isothiocyanate (FITC) modeling produced a Deff range of 1E-20 to 5E-20 cm2/s at a 1 µm cell monolayer thickness and a Deff constant near 5E-29 cm2/s at a 5 µm cell monolayer thickness. Both fluorescein and sodium fluorescein (NaFl) modeling at the 1 and 5 µm thicknesses did not produce simulations that closely resembled the HBMEC in vitro model. Overall, it is possible that the fluorescent intensity noted with fluorescein and NaFl may be better explained by a mechanism other than passive diffusion. Simulations of FITC diffusion produced a narrow range of Deff constants that closely matched the in vitro HBMEC fluorescent intensity.

Modeling techniques such as molecular dynamics are often complicated and computationally expensive. This paper discusses agent-based modeling (ABM) as an alternative modeling approach that allows for any level of modeling complexity at a reasonable computational effort. The ABM framework is generally applicable for modeling complex systems. More specifically, ABM makes it possible to impose simple behaviors on individual “agents” which interact to produce an overall complex system behavior, thus facilitating the modeling of complex systems. To demonstrate the feasibility of ABM, we apply the approach to two problems: gas separation in an inorganic-organic membrane and hydrogen capture in a Metal-Organic Framework (MOF). The data collected from the models is consistent with experimental data suggesting that ABM is an effective modeling tool.

We present a holistic machine-learning framework for rapid screening of bimetallic catalysts with the aid of the descriptor-based kinetic analysis. A catalyst database, which contains the adsorption energies of *CO and *OH on {111}-terminated model alloy surfaces and fingerprint features of active sites from density functional theory calculations with the semi-local generalized gradient approximation (GGA), is established and used in optimizing the structural and weight parameters of artificial neural networks. The fingerprint descriptors, rooted at the d-band chemisorption theory and its recent developments, include the sp-band and d-band characteristics of an adsorption site together with tabulated properties of host-metal atoms. Using methanol electro-oxidation as the model reaction, the machine-learning model trained with the existing dataset of ∼1000 idealized alloy surfaces can capture complex, non-linear adsorbate/metal interactions with the RMSE ∼ 0.2 eV and shows predictive power in exploring the immense chemical space of bimetallic catalysts. Feature importance analysis sheds light on the underlying factors that govern the adsorbate/metal interactions and provides the physical origin of bimetallics in breaking energy-scaling constraints of *CO and *OH, the two most commonly used reactivity descriptors in heterogeneous catalysis.

Oils and fats are important raw materials in food products, animal feed, cosmetics, and pharmaceuticals among others. The market today is dominated by oils derive, d from African palm, soybean, oilseed and animal fats. Colombia&rsquo, s Amazon region has endemic palms such as Euterpe precatoria (a&ccedil, ai), Oenocarpus bataua (patawa), and Mauritia flexuosa (buriti) which grow in abundance and produce a large amount of ethereal extract. However, as these oils have never been used for any economic purpose, little is known about their chemical composition or their potential as natural ingredients for the cosmetics or food industries. In order to fill this gap, we decided to characterize the lipids present in the fruits of these palms. We began by extracting the oils using mechanical and solvent-based approaches. The oils were evaluated by quantifying the quality indices and their lipidomic profiles. The main components of these profiles were triglycerides, followed by diglycerides, fatty acids, acylcarnitine, ceramides, ergosterol, lysophosphatidylcholine, phosphatidyl ethanolamine, and sphingolipids. The results suggest that solvent extraction helped increase the diglyceride concentration in the three analyzed fruits. Unsaturated lipids were predominant in all three fruits and triolein was the most abundant compound. Characterization of the oils provides important insights into the way they might behave as potential ingredients of a range of products. The sustainable use of these oils may have considerable economic potential.

Effect of steric hindrance resulting from solvent topography on the resultant crystal morphology was examined via cooling crystallization of various carboxylic acids in isomeric butyl and pentyl alcohols. Our experiments show that the magnitude of hindrance is related to the degree of branching at the substituted carbon, with hindrance increasing in the order of 1° < 2° < 3° alcohols. The resulting crystals displayed a trend of low, intermediate, and high aspect ratios, corresponding to 1°, 2°, and 3° alcohols, respectively. In particular, 3° alcohols have a tendency to yield significantly different crystal morphologies, compared to 1° and 2° alcohols. Hence, the position of the hydroxyl functional group plays a major role in enhancing or limiting solute–solvent hydrogen bonding interactions and thereby influencing the resultant crystal morphology. A simple molecular model, with succinic acid as test case, was used to demonstrate the extended hydrogen bonding network and surface chemistry binding at the d...

In biological cells, nuclear pore complexes (NPCs) embedded in cell membranes are capable of controlling the flow of ions, for example, Na+ , K+ , and Ca2+ by responding to stimuli, for example, pH and voltage. Inspired by NPCs, researchers have been endeavoring to develop nanogates to achieve the control of ion transport, but the developed nanogates only have a low factor of change in ion currents due to ON/OFF switching. As such nanopores with high temperature and pH responsivities were developed in this work. According to the experimental results, at a voltage of 3 V, the change in ion currents due to pH change is up to a factor of 170, which is remarkably high compared to other nanogates reported. Quantum chemical (QC) calculation results show that a protonated cytosine molecule (C+ ) and an unprotonated cytosine molecule (C) form three pairs of hydrogen bonds and consequently a nucleobase pair, CC+ , leading to the binding of various strands, assembly of a strand net, and blockage of ion transport. The nanogate developed is capable of responding to temperature change. At a voltage of 3 V, the factor of change in ion currents in response to temperature variation is as high as 110. Further experiments were performed to investigate the influence of the NaCl concentrations and small opening diameters exerted on nanogate performance.

Molecular functionalization of porphyrins opens countless new opportunities in tailoring their physicochemical properties for light-harvesting applications. However, the immense materials space spanned by a vast number of substituent ligands and chelating metal ions prohibits high-throughput screening of combinatorial libraries. In this work, machine-learning algorithms equipped with the domain knowledge of chemical graph theory were employed for predicting the energy gaps of12 000 porphyrins from the Computational Materials Repository. Among a variety of graph-based molecular descriptors, the electrotopological-state index, which encodes electronic and topological structure information, captures the energy gaps of porphyrins with a prediction RMSE0.06 eV. Importantly, feature sensitivity analysis suggests that the carbon structural motif in methine bridges connected to the anchor group predominantly influences the energy gaps of porphyrins, consistent with the spatial distribution of their frontier molecular orbitals from quantum-chemical calculations.

Ionic liquids (ILs) show brilliant performance in separating gas impurities, but few researchers have performed an in-depth exploration of the bulk and interface behavior of penetrants and ILs thoroughly. In this research, we have performed a study on both molecular dynamics (MD) simulation and quantum chemical (QC) calculation to explore the transport of acetylene and ethylene in the bulk and interface regions of 1-butyl-3-methylimidazolium tetrafluoroborate ([BMIM]-[BF4]). The diffusivity, solubility and permeability of gas molecules in the bulk were researched with MD simulation first. The subdiffusion behavior of gas molecules is induced by coupling between the motion of gas molecules and the ions, and the relaxation processes of the ions after the disturbance caused by gas molecules. Then, QC calculation was performed to explore the optical geometry of ions, ion pairs and complexes of ions and penetrants, and interaction potential for pairs and complexes. Finally, nonequilibrium MD simulation was performed to explore the interface structure and properties of the IL-gas system and gas molecule behavior in the interface region. The research results may be used in the design of IL separation media.

In this chapter, tablet designs (oral drug dosage forms), which could lead to desired drug release profiles, are discussed. The targeted drug release profiles include constant (i.e., zero-order) release and pulsatile release. The proposed designs are supported through mathematical modeling and simulations. Since the excipients affect the performance of the tablet, a computer-aided molecular design approach is proposed for generating the polymer (an excipient). Finally, a desirability function has been used to rank the generated polymers based on a desired property value. The proposed new polymers need further investigation to be practical for oral drug delivery.

The advent of numerous technological platforms for genome sequencing has led to increasing understanding and construction of metabolic networks. A popular system engineering strategy is used to analyze microbial metabolic networks is flux balance analysis (FBA). In recent times, there has been a lot of interest in the study of the metabolic network dynamics when genes are overexpressed in the system. Herein, an optimization framework, which employs dynamic flux balance analysis (DFBA) is proposed for predicting ethanol concentration profiles in glycerol fermentations using Escherichia coli. In silico results were experimentally validated by overexpressing alcohol/acetaldehyde dehydrogenase adhE, pyruvate kinase pykF, pyruvate formate-lyase pflB and isoleucine-valine enzymes ilvC and llvL.

The depletion of fossil fuel and the environmental impact of using fossil fuel as a main energy source have been subjects of intense research and policy interest in recent years. Pyrolysis of biomass to produce bio-energy is a promising process. However, with the resulting high cost, creating a cost effective chemical plant is very important. A comprehensive process model, which can be used to predict the production from pyrolysis of biomass, is therefore necessary. However, modeling is complex and challenging because of short reaction times, temperatures as high as a thousand degrees Celsius, and biomass of varying or unknown chemical compositions. As such a deterministic model is not capable of representing the pyrolysis reaction system. We propose a new kinetic reaction model, which would account for significant uncertainty. Specifically we have employed fuzzy modeling using the adaptive neuro-fuzzy inference system (ANFIS) in order to describe the pyrolysis of biomass. The resulting model is in better agreement with experimental data than known deterministic models.

A general mixed mechanism model formulated within a grid framework is proposed. In particular, the framework is employed to account for the variability in the diffusion of carbon dioxide and methane in a certain class of inorganic–organic membranes. This variability is a basis for separation of gas mixtures into the various components. Various sizes of the cells within the grid are developed for a series of hybrid inorganic–organic membranes. Parameters in each model are estimated from the permeance results of both carbon dioxide and methane. Acceptable grid models are determined by comparing the estimated parameters of a surface diffusion mechanism with the values reported in the open literature. For each membrane, the model with the minimum number of grid cells is studied. The diffusion in the studied membranes is dominated by activated Knudsen at lower temperatures and limited by surface diffusion when the temperature is above a critical value. Activated Knudsen diffusion is the major mechanism that af...

This paper considers both LCA and LCC of the pyrolysis of switchgrass to use as an energy source in a conventional power plant. The process consists of cultivation, harvesting, transportation, storage, pyrolysis, transportation, and power generation. Here pyrolysis oil is converted to electric power through cocombustion in conventional fossil fuel power plants. Several scenarios are conducted to determine the effect of selected design variables on the production of pyrolysis oil and type of conventional power plants. The set of design variables consist of land fraction, land shape, the distance needed to transport switchgrass to the pyrolysis plant, the distance needed to transport pyrolysis oil to electric generation plant, and the pyrolysis plant capacity. Using an average agriculture land fraction of the United States at 0.4, the estimated cost of electricity from pyrolysis of 5000 tons of switchgrass is the lowest at $0.12 per kwh. Using natural gas turbine power plant for electricity generation, the price of electricity can go as low as 7.70 cent/kwh. The main advantage in using a pyrolysis plant is the negative GHG emission from the process which can define that the process is environmentally friendly. Published version

Recent research efforts have focused on the production of environmentally nonthreatening products, including identifying biosurfactants that can replace conventional surfactants. In order to utilize biosurfactants in different industries such as cosmetic, food or petroleum, it is necessary to understand the underpinnings behind the interactions that could take place for biosurfactants which display potential for interface activity. This work aimed to use molecular dynamics simulations to understand the interactions of rationally obtained peptide sequences from the original sequence of the OmpA gene in Escherichia coli, based on the free energy change (ΔG) during peptide insertion at the water–dodecane interface. Seventeen OmpA–based peptide sequences were selected and analyzed based on their hydropathy index profiles. We found that free energy change due to Columbic interactions and SASA (ΔGCoul/SASA), total free energy change and MW (ΔG/MW), and free energy change due to Coulombic and van der Waals interactions (ΔGCoul/ΔGvdW) ratios could provide a better understating in the contribution of the free energy decrease at the interface. The results indicated that the peptide sequences GKNHDTGVSPVFA and THENQLGAGAFG display biosurfactant potential based on low ΔG per square nanometer, high ΔGCoul/ΔGvdW ratio, clearly defined moieties along its hydrophobic surface and sequence, and the presence of charged residues in the polar head. Clearly defined moieties and SASA were determinant for electrostatic interactions between oil–water interfaces. Experimental validations exhibited that the emulsions prepared remained stable between 3 and 27 h, respectively. Even though the peptide GKNHDTGVSPVFA displays strong interactions at the interface, stabilization times showed that the peptide THENQLGAGAFG exhibited the best performance suggesting that the stability can be better described by kinetic rather than thermodynamic criteria once the emulsion is formed.

We present a machine-learning-augmented chemisorption model that enables fast and accurate prediction of the surface reactivity of metal alloys within a broad chemical space. Specifically, we show that artificial neural networks, a family of biologically inspired learning algorithms, trained with a set of ab initio adsorption energies and electronic fingerprints of idealized bimetallic surfaces, can capture complex, nonlinear interactions of adsorbates (e.g., *CO) on multimetallics with ∼0.1 eV error, outperforming the two-level interaction model in prediction. By leveraging scaling relations between adsorption energies of similar adsorbates, we illustrate that this integrated approach greatly facilitates high-throughput catalyst screening and, as a specific case, suggests promising {100}-terminated multimetallic alloys with improved efficiency and selectivity for CO2 electrochemical reduction to C2 species. Statistical analysis of the network response to perturbations of input features underpins our fundamental understanding of chemical bonding on metal surfaces.