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Chromatography is one of the most valuable techniques chemists possess at their disposal, conducive to everything from developing vaccines, food, beverage, and drug testing to catching criminals. The diverse applications allow it to be used for analytical and preparative purposes. On the other hand, droplet microfluidics has significantly evolved from simple droplet generators to complex and integrated tasks through specially designed networks. Microfluidics finds itself at the center of various Lab-on-Chip studies, enabling single-cell analysis, biochemical synthesis, etc. We demonstrate a microfluidic chromatograph machine that can produce an ordered droplet arrangement for a large number of drops. The droplets are sent into the device using a novel methodology where the conventional droplet train is made into smaller batches. The study describes the use of droplet batch methodology and compares it with the traditional droplet train approach. Using this platform, different droplet sequences are sent through the chromatograph, which preferably allows some droplets to exit first while others take a longer time to flow across the chromatograph based on the droplet properties and device design. The droplet sequences contain various drops; however, the type of drops in these sequences is limited to 2. The chromatograph can handle any number of drops in a single machine is enough for handling diverse droplet sequences. The stability of the microfluidic chromatography is also studied by varying the droplet properties and the droplet batch size.

Silicon and carbon powder pellets were used to study the mechanism of self-propagating high-temperature synthesis (SHS) of SiC. The pellets were quickly heated on a graphite strip to generate beta-SiC without silicon and carbon residues. The SHS process demonstrated an activation enthalpy of approximately 59 kilocalories per mole.

An efficient and environmentally benign synthesis of a new class of acridinedione embedded spirooxindolo/acenaphthenothiopyrrolizidines and spirooxindolo/acenathenoindolizidines has been synthesized in good to excellent yields employing ionic liquid accelerated one-pot [3 + 2]-cycloaddition strategy. The pre-requisite starting substrates, O-acryloyl acridinediones were prepared from dimedone in three good yielding steps, while the 1,3-dipole was derived in situ from isatin/acenaphthenequinone and thiazolidine-4-carboxylc acid/l-pipecolinic acid via decarboxylative condensation. The cycloadduct possesses three stereogenic carbons, one of which is a spiro carbon through the formation of two C–C and one C–N bonds in one-pot synthetic transformation. Geometrical parameters of the synthesized compounds were calculated using the B3LYP/6-311g(d,p) level of theory. The activity of these molecules was evaluated against main protease of COVID-19 to screen them for their inhibitor efficiency. In order to get a broad understanding of the interactions of these synthesized ligands, a detailed molecular docking analysis was performed. Molecular docking analysis shows that compound 8b has the highest binding affinity toward the protein. The compound can be a potential candidate for the treatment of COVID-19.

This work, in a pioneering approach, attempts to build a biometric system that works purely based on the fluid mechanics governing exhaled breath. We test the hypothesis that the structure of turbulence in exhaled human breath can be exploited to build biometric algorithms. This work relies on the idea that the extrathoracic airway is unique for every individual, making the exhaled breath a biomarker. Methods including classical multi-dimensional hypothesis testing approach and machine learning models are employed in building user authentication algorithms, namely user confirmation and user identification. A user confirmation algorithm tries to verify whether a user is the person they claim to be. A user identification algorithm tries to identify a user's identity with no prior information available. A dataset of exhaled breath time series samples from 94 human subjects was used to evaluate the performance of these algorithms. The user confirmation algorithms performed exceedingly well for the given dataset with over 97% true confirmation rate. The machine learning based algorithm achieved a good true confirmation rate, reiterating our understanding of why machine learning based algorithms typically outperform classical hypothesis test based algorithms. The user identification algorithm performs reasonably well with the provided dataset with over 50% of the users identified as being within two possible suspects. We show surprisingly unique turbulent signatures in the exhaled breath that have not been discovered before. In addition to discussions on a novel biometric system, we make arguments to utilise this idea as a tool to gain insights into the morphometric variation of extrathoracic airway across individuals. Such tools are expected to have future potential in the area of personalised medicines.

An edge termination technique for 4H-silicon carbide devices based on the self-aligned implantation of a neutral ion at the edge results in higher breakdown voltages than those obtained for mesa edge terminated diodes. Experimental study of argon implantation at the edges of Schottky barrier diodes shows a small spread in the breakdown voltage suggesting the removal of edge effects in the terminated diodes. The reduced edge electric field is related to the creation of a thin high resistivity layer at the surface beyond the edges of the diodes due to argon implantation.

We present machine learning models based on kernel-ridge regression for predicting x-ray photoelectron spectra of organic molecules originating from the K -shell ionization energies of carbon (C), nitrogen (N), oxygen (O), and fluorine (F) atoms. We constructed the training dataset through high-throughput calculations of K -shell core-electron binding energies (CEBEs) for 12 880 small organic molecules in the bigQM7 ω dataset, employing the Δ-SCF formalism coupled with meta-GGA-DFT and a variationally converged basis set. The models are cost-effective, as they require the atomic coordinates of a molecule generated using universal force fields while estimating the target-level CEBEs corresponding to DFT-level equilibrium geometry. We explore transfer learning by utilizing the atomic environment feature vectors learned using a graph neural network framework in kernel-ridge regression. Additionally, we enhance accuracy within the Δ-machine learning framework by leveraging inexpensive baseline spectra derived from Kohn–Sham eigenvalues. When applied to 208 combinatorially substituted uracil molecules larger than those in the training set, our analyses suggest that the models may not provide quantitatively accurate predictions of CEBEs but offer a strong linear correlation relevant for virtual high-throughput screening. We present the dataset and models as the Python module, cebeconf , to facilitate further explorations.

Experimental determination of mechanical properties of rubber composites, such as tensile strength and hardness, involves complex multistage preparation procedures that are laborious and expensive. In this study, a hybrid filler of carbon black (CB) along with a sustainable filler of lignin is added for reinforcement in the nitrile butadiene rubber (NBR) matrix, with the total filler content varying from 10 parts per hundred rubber (phr) to 80 phr. This work aims to develop a data-driven predictive model for the mechanical properties of rubber composites. An artificial neural network (ANN) model using multilayer feed-forward back-propagation has been created to forecast the tensile strength ( Ts ) and hardness ( Hd ) of rubber composites. The model predicts the uniaxial tensile response and hardness using input parameters that include total filler and lignin loading levels. The effectiveness of the suggested prediction method was demonstrated by statistical analysis using confidence intervals, showing a prediction error between 5.47% and 3.23% for the Ts and between 3.03% and 1.85% for Hd at 95% confidence intervals. A sustainable green band could be defined in the developed model, which is handy for designers to replace CB with lignin in various NBR based products, such as hoses, seals, etc., without compromising on tensile strength and hardness.