Molecular dynamics simulation-based study to analyse the properties of entrapped water between gold and graphene 2D interfaces.

Heterostructures based on graphene and other 2D materials have received significant attention in recent years. However, it is challenging to fabricate them with an ultra-clean interface due to unwanted foreign molecules, which usually get introduced during their transfer to a desired substrate. Clean nanofabrication is critical for the utilization of these materials in 2D nanoelectronics devices and circuits, and therefore, it is important to understand the influence of the "non-ideal" interface. Inspired by the wet-transfer process of the CVD-grown graphene, herein, we present an atomistic simulation of the graphene-Au interface, where water molecules often get trapped during the transfer process. By using molecular dynamics (MD) simulations, we investigated the structural variations of the trapped water and the traction-separation curve derived from the graphene-Au interface at 300 K. We observed the formation of an ice-like structure with square-ice patterns when the thickness of the water film was <5 Å. This could cause undesirable strain in the graphene layer and hence affect the performance of devices developed from it. We also observed that at higher thicknesses the water film is predominantly present in the liquid state. The traction separation curve showed that the adhesion of graphene is better in the presence of an ice-like structure. This study explains the behaviour of water confined at the nanoscale region and advances our understanding of the graphene-Au interface in 2D nanoelectronics devices and circuits.

Coarse grained MD simulation of bulk and interfacial behavior of mixture of CTAB/SDS surfactants.

CONTEXT: This study involves simulating the process of inhibiting corrosion through the formation of micelles by surfactants and their deposition on iron (Fe) surfaces. The primary focus is on examining CTAB/SDS mixtures in aqueous solutions with different concentrations. Micelle properties, including size, shape, aggregation number, cluster size, and surfactant diffusion, were calculated and validated with experimental data. The coarse-grained Fe surface was modeled and validated against experimental water contact-angle data. Subsequently, the deposition of CTAB/SDS mixtures on the Fe surface and air-water interface was studied systematically. We found that the relative ratio of CTAB/SDS in the solution directly influences surfactant deposition behavior, which might impact the corrosion inhibition efficiency. METHODS: All the MD simulations were performed using the GROMACS software with MARTINI2 force field and Martini polar water. The molecules are packed using PACKMOL software. Both NVT and NPT simulations are caried out at temperature and pressure of 303 K and 1 bar respectively, with a nonbonded interaction cut-off (rcut) of 1.1 nm. The LJ potential was shifted from 0.9 nm to rcut, while the electrostatic potential was shifted from 0.0 nm to rcut. For electrostatics, reaction-field coulomb type is used, relative dielectric constant (epsilon-r) and the reaction field dielectric constant (epsilon-rf) are equal to 2.5 and infinity respectively. The dielectric constant below rcut is epsilon-r, and beyond the cut-off is epsilon-rf. Coulomb-modifier used as potential-shift which leads to shift in the coulomb potential by a constant such that it is zero at the rcut. This makes the potential of the integral of the force . The neighbor list was updated every 10 steps, employing a neighbor list cut-off equal to rcut. Using a polar water model, we used a constant time step of 0.02 ps throughout the simulation. The used epsilon-r = 2.5, is recommended for polar water.

PDMS nanoparticles-decorated PDMS substrate promotes adhesion, proliferation and differentiation of skin cells.

Polydimethylsiloxane (PDMS) is known to be a common substrate for various cell culture-based applications. However, native PDMS is not very conducive for cell culture and hence, surface modification via cell adhesion moieties is generally needed to make it suitable especially for long-term cell culture. To address this issue, we propose to coat PDMS nanoparticles (NPs) on the surface of PDMS film to improve adhesion, proliferation and differentiation of skin cells. The proposed modification strategy introduces necessary nanotopography without altering the surface chemical properties of PDMS. Due to resemblance in the mechanical properties of PDMS with skin, PDMS NPs can recreate the native extracellular nanoenvironment of skin on the PDMS surface and provide anchoring sites for skin cells to adhere and grow. Human keratinocytes, representing 95% of the epidermal skin cells maintained their characteristic well-spread morphology with the formation of interconnected cell-sheets on this coated PDMS surface. Moreover, our in vitro immunofluorescence studies confirmed expression of distinctive epidermal protein markers on the coated surface indicating close resemblance with the native skin epidermis. Conclusively, our findings suggest that introducing nanotopography via PDMS NPs can be an effective strategy for emulating the native cellular functions of keratinocytes on PDMS based cell culture devices.

Effects of Segregation on the Catalytic Properties of AgAuCuPdPt High-Entropy Alloy for CO Reduction Reaction.

Multicomponent alloys are promising catalysts for diverse chemical conversions, owing to the ability to tune their vast compositional space to maximize catalytic activity and product selectivity. However, elemental segregation, whereby the surface or grain boundaries of the material are enriched in a few elements, is a physically observed phenomenon in such alloys. Such segregation alters not only the composition but also the kinds of catalytically active sites present at the surface. Thus, elemental segregation, which can be achieved via various processing techniques, can be used as an additional knob in searching for alloy compositions that are both active and selective for a target chemical conversion. We demonstrate this using molecular simulations, machine learning, and Bayesian optimization to search for both random solid solution and "segregated" AgAuCuPdPt alloy compositions that are potentially active and selective for CO reduction reaction (CORR). Finally, we validate our findings by computing the reaction-free energy landscape for the CORR on the optimal alloy compositions via density functional theory calculations.

Classification of tastants: A deep learning based approach.

Predicting the taste of molecules is of critical importance in the food and beverages, flavor, and pharmaceutical industries for the design and screening of new tastants. In this work, we have built deep learning models to classify sweet, bitter, and umami molecules- the three basic tastes whose sensation is mediated by G protein-coupled receptors. An extensive dataset containing 1466 bitter, 1764 sweet, and 238 umami tastants was curated from existing literature. We analyzed the chemical characteristics of the molecules, with special focus on the presence of different functional groups. A deep neural network model based on molecular descriptors and a graph neural network model were trained for taste prediction. The class imbalance due to fewer umami molecules was tackled using special sampling techniques. Both models show comparable performance during evaluation, but the graph-based model can learn task-specific representations from the molecular structure without requiring handcrafted features. We further explain the deep neural network predictions using Shapley additive explanations. Finally, we demonstrated the applicability of the models by screening bitter, sweet, and umami molecules from a large food database. This study develops an in-silico approach to classify molecules based on their taste by leveraging the recent progress in deep learning, which can serve as a powerful tool for tastant design.

Integrated point-of-care RT-PCR methods during and after COVID-19 pandemic.

The COVID-19 pandemic has taken the world by surprise and people and organisations worldwide worked in some way or the other to combat the spread; isolate from the infected and get back to normal life, as it was before the pandemic hit. In this regard, the diagnosis of COVID-19 was at the centre of control and prevention and have seen a vehement change in every aspect, especially development of point-of-care testing for better and quick diagnosis. Among different types of techniques developed, the most important was the RT-PCR method of detection which detects nucleic acid of the virus in samples. RT-PCR is a laboratory-based method requiring trained professionals and precise steps for accurate testing. With the advent and spread of the pandemic, number of RT-PCR diagnostic centres rose significantly, and the detection process became less cumbersome, easy to use, ability to handle large volume of samples, more accurate, less time-consuming, and cost-effective. Different industries developed RT-PCR kits, reducing the efforts to prepare laboratory samples. Machines were employed for labour-driven tasks in PCR testing. In addition, new age technologies such as artificial intelligence, IoT, digital systems were combined with RT-PCR for accurate and easy testing. In this review, point-of-care RT-PCR methods, when the COVID-19 started, and the methods now, has been compared on the basis of technological advancements.

Zero-shot transfer learned generic AI models for prediction of optimally ripe climacteric fruits.

Ideally, ripe fruits offer appropriate nutritional content and best quality in terms of taste and flavour. Prediction of ripe climacteric fruits acts as the main marketing indicator for quality from the consumer perspective and thus renders it a genuine industrial concern for all the stakeholders of the fruit supply chain. However, the building of fruit-specific individual model for the prediction of ripeness level remains an existing challenge due to the scarcity of sufficient labeled experimental data for each fruit. This paper describes the development of generic AI models based on the similarity in physico-chemical degradation phenomena of climacteric fruits for prediction of 'unripe' and 'ripe' levels using 'zero-shot' transfer learning techniques. Experiments were performed on a variety of climacteric and non-climacteric fruits, and it was observed that transfer learning works better for fruits within a cluster (climacteric fruits) as compared to across clusters (climacteric to non-climacteric fruits). The main contributions of this work are two-fold (i) Using domain knowledge of food chemistry to label the data in terms of age of the fruit, (ii) We hypothesize and prove that the zero-shot transfer learning works better within a set of fruits, sharing similar degradation chemistry depicted by their visual properties like black spot formations, wrinkles, discoloration, etc. The best models trained on banana, papaya and mango dataset resulted in s zero-shot transfer learned accuracies in the range of 70 to 82 for unknown climacteric fruits. To the best of our knowledge, this is the first study to demonstrate the same.

Modified variational autoencoder for inversely predicting plasmonic nanofeatures for generating structural color.

We apply a modified variational autoencoder (VAE) regressor for inversely retrieving the topological parameters of the building blocks of plasmonic composites for generating structural colors as per requirement. We demonstrate results of a comparison study between inverse models based on generative VAEs as well as conventional tandem networks that have been favored traditionally. We describe our strategy for improving the performance of our model by filtering the simulated dataset prior to training. The VAE- based inverse model links the electromagnetic response expressed as the structural color to the geometrical dimensions from the latent space using a multilayer perceptron regressor and shows better accuracy over a conventional tandem inverse model.

Elucidating collective translocation of nanoparticles across the skin lipid matrix: a molecular dynamics study.

The top layer of skin, the stratum corneum, provides a formidable barrier to the skin. Nanoparticles are utilized and further explored for personal and health care applications related to the skin. In the past few years, several researchers have studied the translocation and permeation of nanoparticles of various shapes, sizes, and surface chemistry through cell membranes. Most of these studies focused on a single nanoparticle and a simple bilayer system, whereas skin has a highly complex lipid membrane architecture. Moreover, it is highly unlikely that a nanoparticle formulation applied on the skin will not have multiple nanoparticle-nanoparticle and skin-nanoparticle interactions. In this study, we have utilized coarse-grained MARTINI molecular dynamics simulations to assess the interactions of two types (bare and dodecane-thiol coated) of nanoparticles with two models (single bilayer and double bilayer) of skin lipid membranes. The nanoparticles were found to be partitioned from the water layer to the lipid membrane as an individual entity as well as in the cluster form. It was discovered that each nanoparticle reached the interior of both single bilayer and double bilayer membranes irrespective of the nanoparticle type and concentration, though coated particles were observed to efficiently traverse across the bilayer when compared with bare particles. The coated nanoparticles also created a single large cluster inside the membrane, whereas the bare nanoparticles were found in small clusters. Both the nanoparticles exhibited preferential interactions with cholesterol molecules present in the lipid membrane as compared to other lipid components of the membrane. We have also observed that the single membrane model exhibited unrealistic instability at moderate to higher concentrations of nanoparticles, and hence for translocation study, a minimum double bilayer model should be employed.

Subcutaneous Drug Delivery: A Review of the State-of-the-Art Modeling and Experimental Techniques.

Delivery of drug formulations through the subcutaneous route is a widely used modality for the treatment of several diseases, such as diabetes and auto-immune conditions. Subcutaneous injections are typically used to inject low-viscosity drugs in small doses. However, for new biologics, there is a need to deliver drugs of higher viscosity in large volumes. The response of subcutaneous tissue to such high-volume doses and higher viscosity injections is not well understood. Animal models have several drawbacks such as relevance to humans, lack of predictive power beyond the immediate population studied, cost, and ethical considerations. Therefore, a computational framework that can predict the tissue response to subcutaneous injections would be a valuable tool in the design and development of new devices. To model subcutaneous drug delivery accurately, one needs to consider: (a) the deformation and damage mechanics of skin layers due to needle penetration and (b) the coupled fluid flow and deformation of the hypodermis tissue due to drug delivery. The deformation of the skin is described by the anisotropic, hyper-elastic, and viscoelastic constitutive laws. The damage mechanics is modeled by using appropriate damage criteria and damage evolution laws in the modeling framework. The deformation of the subcutaneous space due to fluid flow is described by the poro-hyperelastic theory. The objective of this review is to provide a comprehensive overview of the methodologies used to model each of the above-mentioned aspects of subcutaneous drug delivery. We also present an overview of the experimental techniques used to obtain various model parameters.

Coronavirus: a comparative analysis of detection technologies in the wake of emerging variants.

An outbreak of the coronavirus disease caused by a novel pathogen created havoc and continues to affect the entire world. As the pandemic progressed, the scientific community was faced by the limitations of existing diagnostic methods. In this review, we have compared the existing diagnostic techniques such as reverse transcription polymerase chain reaction (RT-PCR), antigen and antibody detection, computed tomography scan, etc. and techniques in the research phase like microarray, artificial intelligence, and detection using novel materials; on the prospect of sample preparation, detection procedure (qualitative/quantitative), detection time, screening efficiency, cost-effectiveness, and ability to detect different variants. A detailed comparison of different techniques showed that RT-PCR is still the most widely used and accepted coronavirus detection method despite certain limitations (single gene targeting- in context to mutations). New methods with similar efficiency that could overcome the limitations of RT-PCR may increase the speed, simplicity, and affordability of diagnosis. In addition to existing devices, we have also discussed diagnostic devices in the research phase showing high potential for clinical use. Our approach would be of enormous benefit in selecting a diagnostic device under a given scenario, which would ultimately help in controlling the current pandemic caused by the coronavirus, which is still far from over with new variants emerging.

Multiscale modeling of skin mechanical Behavior: Effect of dehydrating agent on Collagen's mechanical properties.

The dermis, second layer of human skin, is mainly responsible for mechanical response of the skin. The unique viscoelastic nature of this layer arises from the characteristic hierarchical structure of collagen at various length scales. The effect of topical formulation on skin's mechanical properties of great importance for several personal-care applications. Understanding the transport of an active ingredient across skin layer and its effects on the structure of collagen assembly is crucial for successful design of these applications. In this study, we report a multiscale modelling framework mimicking the skin's mechanical behavior. The framework captures the details from the nanoscale (tropocollagen) to microscale (fibers). At first, atomistic molecular dynamics simulations (MDS) of tropocollagen (TC) molecules of various lengths (∼100 nm) were performed to obtain the molecular modulus of TC. The stress-strain response data obtained from these simulations, were utilized in macroscopic models of fibrils and fibers. The modulus obtained from the mentioned framework was in good agreement with earlier reported experimental data. Further, we have utilized this framework to show the effect of dehydrating agent on skin's mechanical response. The hydration effect is utilized in many anti-ageing strategies to improve the overall mechanical property of skin. We showed that on incorporation of hydrating agent, the collagen structure changes significantly at molecular scale which effects the overall response of the skin at macroscopic scale. The reported multiscale framework can further be explored to gain insights into interlinked properties of collagen at much larger scales without extensive molecular simulations and detailed experiments.

Development of an insilico model of eccrine sweat using molecular modelling techniques.

Eccrine sweat is an ideal surrogate diagnostic biofluid for physiological and metabolic biomarkers for wearable biosensor design. Its periodic and non-invasive availability for candidate analytes such as glucose and cortisol along with limited correlation with blood plasma is of significant research interest. An insilico model of eccrine sweat can assist in the development of such wearable biosensors. In this regard, molecular modelling can be employed to observe the most fundamental interactions. Here, we determine a suitable molecular model for building eccrine sweat. The basic components of sweat are water and sodium chloride, in which glucose and other analytes are present in trace quantities. Given the wide range of water models available in the molecular dynamics space, in this study, we first validate the water models. We use three compounds to represent the base to build bulk sweat fluid and validate the force fields. We compare the self-diffusivity of water, glucose, sodium, and chloride ions as well as bulk viscosity values and present the results which are > 90% accurate as compared with the available literature. This validated insilico eccrine sweat model can serve as an aid to expedite the development de novo biosensors by addition of other analytes of interest e.g. cortisol, uric acid etc., simulate various temperatures and salt concentrations, expand search space for screening candidate target receptors by their binding affinity and assess the interference between competing species via simulations.

Systematic Identification of Atom-Centered Symmetry Functions for the Development of Neural Network Potentials.

Neural network potentials are emerging as promising classical force fields that can enable long-time and large-length scale simulations at close to ab initio accuracies. They learn the underlying potential energy surface by mapping the Cartesian coordinates of atoms to system energies using elemental neural networks. To ensure invariance with respect to system translation, rotation, and atom index permutations, in the Behler-Parrinnello type of neural network potential (BP-NNP), the Cartesian coordinates of atoms are transformed into "structural fingerprints" using atom-centered symmetry functions (ACSFs). Development of an accurate BP-NNP for any chemical system critically relies on the choice of these ACSFs. In this work, we have proposed a systematic framework for the identification of an optimal set of ACSFs for any target system, which not only considers the diverse atomic environments present in the training dataset but also inter-ACSF correlations. Our method is applicable to different kinds of ACSFs and across diverse chemical systems. We demonstrate this by building accurate BP-NNPs for water and Cu2S systems.

Eccrine Sweat Molecular Model for Development of de novo Biosensors.

In this paper, we present a validated, novel, in silico molecular dynamics (MD) model of eccrine sweat with approx. 35k atoms developed using Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) program. CHARMMS36m force field for constituent atoms and SPC/E water model are used to develop this model. The model outputs transport properties such as self-diffusivity computed using mean squared displacement and bulk viscosity computed via Green-Kubo correlations, which are compared with existing literature values and experimental studies and presented. This validated model is intended to serve as a tool to develop eccrine sweat based biosensors.

A simple, robust and scalable route to prepare sub-50 nm soft PDMS nanoparticles for intracellular delivery of anticancer drugs.

Soft nanoparticles (NPs) have recently emerged as a promising material for intracellular drug delivery. In this regard, NPs derived from polydimethylsiloxane (PDMS), an FDA approved polymer can be a suitable alternative to conventional soft NPs due to their intrinsic organelle targeting ability. However, the available synthesis methods of PDMS NPs are complicated or require inorganic fillers, forming composite NPs and compromising their native softness. Herein, for the first time, we present a simple, robust and scalable strategy for preparation of virgin sub-50 nm PDMS NPs at room temperature. The NPs are soft in nature, hydrophobic and about 30 nm in size. They are stable in physiological medium for two months and biocompatible. The NPs have been successful in delivering anticancer drug doxorubicin to mitochondria and nucleus of cervical and breast cancer cells with more than four-fold decrease in IC50 value of doxorubicin as compared to its free form. Furthermore, evaluation of cytotoxicity in reactive oxygen species detection, DNA fragmentation, apoptosis-associated gene expression and tumor spheroid growth inhibition demonstrate the PDMS NPs to be an excellent candidate for delivery of anticancer drugs in mitochondria and nucleus of cancer cells.

Investigating the nanostructure of a CER[NP]/CER[AP]-based stratum corneum lipid matrix model: A combined neutron diffraction & molecular dynamics simulations approach.

The human skin provides a physiochemical and biological protective barrier due to the unique structure of its outermost layer known as the Stratum corneum. This layer consists of corneocytes and a multi-lamellar lipid matrix forming a composite, which is a major determining factor for the barrier function of the Stratum corneum. A substantiated understanding of this barrier is necessary, as controlled breaching or modulation of the same is also essential for various health and personal care applications such as topical drug delivery and cosmetics to a name few. In this study, we discuss the state-of-the-art of neutron diffraction techniques, using specifically deuterated lipids, combined with the information obtained from molecular models using molecular dynamics simulations, to understand the structure and barrier function of the Stratum corneum lipid matrix. As an example, the effect of ceramide concentration on a lipid lamella system consisting of CER[NP]/CER[AP]/Cholesterol/free fatty acid (deprotonated) is studied. This study demonstrates the usefulness of the combined approach of neutron diffraction and molecular dynamics simulations for effective analysis of the model systems created for the Stratum corneum lipid matrix. The optimization of force fields by comparison with experimental data is furthermore an important step in the direction of providing a predictive quality.

Peptide mediated colorimetric detection of SARS-CoV-2 using gold nanoparticles: a molecular dynamics simulation study.

The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has necessitated the development of a rapid, simple yet selective naked-eye detection methodology that does not require any advanced instrumental techniques. In this study, we report our computational findings on the detection of SARS-CoV-2 using peptide- functionalized gold nanoparticles (GNPs). The peptide has been screened from angiotensin-converting enzyme 2 (ACE2) receptor situated on the surface of the host cell membrane which interacts with the spike protein of SARS-CoV-2, resulting entry of the virus into the host cell. As a result, the peptide-functionalized GNPs possess excellent affinity towards the spikes of SARS-CoV-2 and readily get aggregated once exposed to SARS-CoV-2 antigen or virus. The stability of the peptides on the surface of GNPs and their interaction with the spike protein of the virus have been investigated using coarse-grained molecular dynamic simulations. The potential of mean force calculation of spike protein confirmed strong binding between peptide and receptor-binding domain (RBD) of spike protein. Our in silico results demonstrate the potential of the peptide-functionalized GNPs in the development of simple and rapid colorimetric biosensors for clinical diagnosis.

Leveraging long short-term memory (LSTM)-based neural networks for modeling structure-property relationships of metamaterials from electromagnetic responses.

We report a neural network model for predicting the electromagnetic response of mesoscale metamaterials as well as generate design parameters for a desired spectral behavior. Our approach entails treating spectral data as time-varying sequences and the inverse problem as a single-input multiple output model, thereby compelling the network architecture to learn the geometry of the metamaterial designs from the spectral data in lieu of abstract features.