Enhanced Superconducting Diode Effect due to Coexisting Phases.

The superconducting diode effect refers to an asymmetry in the critical supercurrent J_{c}(n[over ^]) along opposite directions, J_{c}(n[over ^])≠J_{c}(-n[over ^]). While the basic symmetry requirements for this effect are known, it is, for junction-free systems, difficult to capture within current theoretical models the large current asymmetries J_{c}(n[over ^])/J_{c}(-n[over ^]) recently observed in experiment. We here propose and develop a theory for an enhancement mechanism of the diode effect arising from spontaneous symmetry breaking. We show-both within a phenomenological and a microscopic theory-that there is a coupling of the supercurrent and the underlying symmetry-breaking order parameter. This coupling can enhance the current asymmetry significantly. Our work might not only provide a possible explanation for recent experiments on trilayer graphene but also pave the way for future realizations of the superconducting diode effect with large current asymmetries.

Two-Legged Molecular Walker and Curvature: Mechanochemical Ring Migration on Graphene.

Attaining controllable molecular motion at the nanoscale can be beneficial for multiple reasons, spanning from optoelectronics to catalysis. Here we study the movement of a two-legged molecular walker by modeling the migration of a phenyl aziridine ring on curved graphene. We find that directional ring migration can be attained on graphene in the cases of both 1D (wrinkled/rippled) and 2D (bubble-shaped) curvature. Using a descriptor approach based on graphene's frontier orbital orientation, we can understand the changes in binding energy of the ring as it translates across different sites with variable curvature and the kinetic barriers associated with ring migration. Additionally, we show that the extent of covalent bonding between graphene and the molecule at different sites directly controls the binding energy gradient, propelling molecular migration. Importantly, one can envision such walkers as carriers of charge and disruptors of local bonding. This study enables a new way to tune the electronic structure of two-dimensional materials for a range of applications.

Prediction of transport behavior of nanoparticles using machine learning algorithm: Physical significance of important features.

There is a rising concern related to the possible risk of human exposure to nanoparticles (NPs). Several studies have reported on the transport behavior of NPs in the porous media under varying conditions. Thus, there is a scope to use this information in a predictive model so that the transport behavior of any un-explored NPs could be predicted. The main focus of his study, therefore, is to apply different machine learning (ML) based models to predict the transport efficiency of a wide range of NPs and to identify the important features. To achieve the objective, first, the dataset is prepared by extracting data from published papers for selected NPs [i.e., silver (nAg), titanium dioxide (nTiO2), zinc oxide (nZnO), graphene oxide (nGO), and etc.]. Then, random forest, XGBoost, and CatBoost algorithms combined with synthetic minority oversampling technique (SMOTE) were applied where retention fraction (RF) is considered as the target feature and particle characteristics (i.e., surface charge, size, concentration), solution chemistry [pH, ionic strength (IS]), porous media properties (grain size, porosity) and flow rate are considered as the training features. The outcome of the study indicates that CatBoost combined with SMOTE performed the best in predicting RF for the entire range of NPs (R2 > 0.89 and MSE < 0.007) as well as for individual NPs. Feature importance analysis indicates four features, namely zeta potential, IS, pH, and particle diameter (the entire range of NPs, nGO, nZnO) or grain size (nAg, nTiO2) have significant weightage (>75%). The result suggests that the features overrule the prediction of transport behavior rather than the types of individual NPs. The relative importance of the features depends on the range of the parameter used. The identified important features are in accordance with the underlying physical process, which makes the prediction model more reliable.

Precocious Puberty.

Precocious puberty is a common presentation to pediatricians with a significant overlap between physiology and pathology. While most girls with precocious puberty have no identifiable cause, boys are more likely to have a pathological cause. The trend of earlier onset of thelarche with slow pubertal tempo has led to a significant increase in the number of girls presenting with precocious puberty. Advanced growth, bone age, uterine maturation, and elevated LH suggest rapidly progressive puberty. The critical issues in evaluating a child presenting with precocious puberty include its confirmation, exclusion of physiological variants, identification of the cause, and determining the need for treatment. Step-wise evaluation with emphasis on clinical parameters provides cost-effective assessment. Gonadotropin-releasing hormone (GnRH) analogs remain the mainstay of treatment for central precocious puberty but should be restricted to individuals with rapidly progressive puberty and compromised final height. The management of rarer forms of peripheral precocious puberty (McCune Albright syndrome, congenital adrenal hyperplasia, and testotoxicosis) involves using experimental drugs under the guidance of specialists.

High-Throughput Computational Screening of Bioinspired Dual-Atom Alloys for CO2 Activation.

CO2 activation is an integral component of thermocatalytic and electrocatalytic CO2 conversion to liquid fuels and value-added chemicals. However, the thermodynamic stability of CO2 and the high kinetic barriers to activating CO2 are significant bottlenecks. In this work, we propose that dual atom alloys (DAAs), homo- and heterodimer islands in a Cu matrix, can offer stronger covalent CO2 binding than pristine Cu. The active site is designed to mimic the Ni-Fe anaerobic carbon monoxide dehydrogenase CO2 activation environment in a heterogeneous catalyst. We find that combinations of early transition metals (TMs) and late TMs embedded in Cu are thermodynamically stable and can offer stronger covalent CO2 binding than Cu. Additionally, we identify DAAs that have CO binding energies similar to Cu, both to avoid surface poisoning and to ensure attainable CO diffusion to Cu sites so that the C-C bond formation ability of Cu can be retained in conjunction with facile CO2 activation at the DAA sites. Machine learning feature selection reveals that the more electropositive dopants are primarily responsible for attaining the strong CO2 binding. We propose seven Cu-based DAAs and two single atom alloys (SAAs) with early TM late TM combinations, (Sc, Ag), (Y, Ag), (Y, Fe), (Y, Ru), (Y, Cd), (Y, Au), (V, Ag), (Sc), and (Y), for facile CO2 activation.

Creating hierarchical pores in metal-organic frameworks via postsynthetic reactions.

Metal-organic frameworks (MOFs) demonstrate promise for a multitude of applications owing to their high porosity and surface area. However, the majority of conventional MOFs possess only micropores with very limited accessibility to substances larger than 2 nm-especially functional biomacromolecules like some proteins. It is challenging to create an appropriately large pore size while avoiding framework collapse in MOFs. Herein, we present the generation of mesopores in microporous MOFs through three facile and effective techniques, namely Soxhlet washing, linker hydrolysis and linker thermolysis. These postsynthetic elimination approaches have been applied in selected MOFs, including PCN-250, PCN-160 and UiO-66, and controllably generate MOFs with hierarchical pores and high stability. Our work demonstrates reproducible and straightforward methods resulting in hierarchically porous materials that possess the benefits of mesoporosity while borrowing the robustness of a micropore framework. All the procedures can be conducted reliably at a multigram scale and operation time less than 6 h, representing a significant effort in the field of MOF synthesis. These hierarchically porous MOFs show great promise in a wide range of applications as efficient adsorbents, catalysts and drug carriers.

Photomechanochemical control over stereoselectivity in the [2 + 2] photodimerization of acenaphthylene.

Tuning solubility and mechanical activation alters the stereoselectivity of the [2 + 2] photochemical cycloaddition of acenaphthylene. Photomechanochemical conditions produce the syn cyclobutane, whereas the solid-state reaction in the absence of mechanical activation provides the anti. When the photochemical dimerization occurs in a solubilizing organic solvent, there is no selectivity. Dimerization in H2O, in which acenaphthylene is insoluble, provides the anti product. DFT calculations reveal that insoluble and solid-state reactions proceed via a covalently bonded excimer, which drives anti selectivity. Alternatively, the noncovalently bound syn conformer is more mechanosusceptible than the anti, meaning it experiences greater destabilization, thereby producing the syn product under photomechanochemical conditions. Cyclobutanes are important components of biologically active natural products and organic materials, and we demonstrate stereoselective methods for obtaining syn or anti cyclobutanes under mild conditions and without organic solvents. With this work, we validate photomechanochemistry as a viable new direction for the preparation of complex organic scaffolds.

Clinicopathological alteration of symptoms with serotype among dengue infected pediatric patients.

Dengue fever is a self-limiting, acute febrile illness caused by an arbovirus. This infection may be asymptomatic or symptomatic with its potential life-threatening form as DHF/DSS. Severe dengue cases occur typically in children due to overproduction of proinflammatory and anti-inflammatory cytokines (called cytokines storm) as well as increased microvascular permeability in them. This study aimed to find circulating dengue serotype and their clinicopathological association among pediatric patients admitted to tertiary care hospitals in Kolkata, India. Overall, 210 patients were approached, among them, 170 dengue suspected children admitted to three tertiary care hospitals were included in this study. Dengue samples were screened for the presence of dengue NS1 antigen and IgM antibodies by enzyme-linked immunosorbent assay. Viral RNA was extracted from NS1 seropositive serum samples and subjected to molecular serotyping by semi-nested reverse-transcription polymerase chain reaction. All patients were followed up for clinical manifestations and biochemical parameters associated with dengue. Cocirculation of all four serotypes was observed and DENV2 was the major circulating strain. Physiological classification of associated clinical symptoms was done as per WHO guideline and represented as a percentage variable. A multivariate logistic regression approach was used for making a regression model including dengue-associated clinical symptoms with dengue positivity or negativity as dependent variables. Thrombocytopenia was observed in 69% of patients and the commonest bleeding manifestation was petechia. Liver function profiles of infected patients were observed during follow-up and represented using a box plot. A significant change in trends of dengue-associated clinical manifestations and differential expression of liver functional profile with different phases of transition of dengue fever was observed in this study population.

Mechanochemical Molecular Migration on Graphene.

In this study, we propose that the curvature of graphene can be exploited to perform directional molecular motion and provide atomistic insights into the curvature-dependent molecular migration through density functional theory calculations. We first reveal the origin of the different migration trends observed experimentally for aromatic molecules with electron-donating and -withdrawing groups on p-doped functionalized graphene. Next, we show that the kinetic barrier for migration depends on the amount and nature of the curvature, that is, positive versus negative curvature. We find that the molecular migration on a wrinkled/rippled graphene sheet preferentially happens from the valley (positive curvature) to the mountain (negative curvature) regions. To understand the origin of such curvature-dependent molecular motion and migrational kinetic barrier trends, we develop a descriptor based on the frontier orbital orientation of graphene. Finally, based on these findings, we predict that time- and space-varying curvature can drive directional molecular motion on graphene and thus further propose that efforts should focus on exploring other two-dimensional materials as active platforms for performing controlled molecular motion.

Unexpected structural/motional mode of water intercalated into an α-crystalline zirconium phosphate deduced by 31 P and 2 H solid-state MAS NMR spectra.

In developing the approach to understanding dynamics of intercalates in layered materials, crystalline-layered zirconium phosphate Zr (HPO4 )2 ·0.35D2 O has been prepared and characterized by the 1 H, 31 P, and 2 H solid-state MAS NMR spectra, including 31 P and 2 H T1 measurements. At temperatures >253 K, the intercalated water shows two spectrally-distinguished deuterons unprecedentedly with different DQCC's and 2 H T1 times, one of which is hydrogen bonded. The collected data allowed to identify an unexpected bonding/dynamic mode of water molecules, which experience fast rotation around the hydrogen bond, formed with a zirconium-coordinated oxygen. The low-temperature 2 H MAS NMR experiments have demonstrated the presence of additional hydrogen bond P(H)O˙˙˙ DO, population of which grows on cooling to 195 K corresponding to the doubly hydrogen-bonded immobile water molecule.

Unraveling the Elastic Properties of (Quasi)Two-Dimensional Hybrid Perovskites: A Joint Experimental and Theoretical Study.

The unique properties of hybrid organic-inorganic perovskites (HOIPs) promise to open doors to next-generation flexible optoelectronic devices. Before such advances are realized, a fundamental understanding of the mechanical properties of HOIPs is required. Here, we combine ab initio density functional theory (DFT) modeling with a diverse set of experiments to study the elastic properties of (quasi)2D HOIPs. Specifically, we focus on (quasi)2D single crystals of phenethylammonium methylammonium lead iodide, (PEA)2PbI4(MAPbI3)n-1, and their 3D counterpart, MAPbI3. We used nanoindentation (both Hertzian and Oliver-Pharr analyses) in combination with elastic buckling instability experiments to establish the out-of-plane and in-plane elastic moduli. The effect of Van der Waals (vdW) forces, different interlayer interactions, and finite temperature are combined with DFT calculations to accurately model the system. Our results reveal a nonmonotonic dependence of both the in-plane and out-of plane elastic moduli on the number of inorganic layers (n) rationalized by first-principles calculations. We discuss how the presence of defects in as-grown crystals and macroscopic interlayer deformations affect the mechanical response of (quasi)2D HOIPs. Comparing the in- and out-of-plane experimental results with the theory reveals that perturbations to the covalent and ionic bonds (which hold a 2D layer together) is responsible for the relative out-of-plane stiffness of these materials. In contrast, we conjecture that the in-plane softness originates from macroscopic or mesoscopic motions between 2D layers during buckling experiments. Additionally, we learn how dispersion and π interactions in organic bilayers can have a determining role in the elastic response of the materials, especially in the out-of-plane direction. The understanding gained by comparing ab initio and experimental techniques paves the way for rational design of layered HOIPs with mechanical properties favorable for strain-intensive applications. Combined with filters for other favorable criteria, e.g., thermal or moisture stability, one can systematically screen viable (quasi)2D HOIPs for a variety of flexible optoelectronic applications.

A unified machine-learning protocol for asymmetric catalysis as a proof of concept demonstration using asymmetric hydrogenation.

Design of asymmetric catalysts generally involves time- and resource-intensive heuristic endeavors. In view of the steady increase in interest toward efficient catalytic asymmetric reactions and the rapid growth in the field of machine learning (ML) in recent years, we envisaged dovetailing these two important domains. We selected a set of quantum chemically derived molecular descriptors from five different asymmetric binaphthyl-derived catalyst families with the propensity to impact the enantioselectivity of asymmetric hydrogenation of alkenes and imines. The predictive power of the random forest (RF) built using the molecular parameters of a set of 368 substrate-catalyst combinations is found to be impressive, with a root-mean-square error (rmse) in the predicted enantiomeric excess (%ee) of about 8.4 ± 1.8 compared to the experimentally known values. The accuracy of RF is found to be superior to other ML methods such as convolutional neural network, decision tree, and eXtreme gradient boosting as well as stepwise linear regression. The proposed method is expected to provide a leap forward in the design of catalysts for asymmetric transformations.

The thermally induced decarboxylation mechanism of a mixed-oxidation state carboxylate-based iron metal-organic framework.

Investigations into a thermally generated decarboxylation mechanism for metal site activation and the generation of mesopores in a carboxylate iron-based MOF, PCN-250, have been conducted. PCN-250 exhibits an interesting oxidation state change during thermal treatment under inert atmospheres or vacuum conditions, transitioning from an Fe(iii)3 cluster to a Fe(ii)Fe(iii)2 cluster. To probe this redox event and discern a mechanism of activation, a combination of thermogravimetric analysis, gas sorption, scanning electron microscopy, 57Fe Mössbauer spectroscopy, gas chromatography-mass spectrometry, and X-ray diffraction studies were conducted. The results suggest that the iron-site activation occurs due to ligand decarboxylation above 200 °C. This is also consistent with the generation of a missing cluster mesoporous defect in the framework. The resulting mesoporous PCN-250 maintains high thermal stability, preserving crystallinity after multiple consecutive high-temperature regeneration cycles. Additionally, the thermally reduced PCN-250 shows improvements in the total uptake capacity of methane and CO2.

Suspension Processing of Microporous Metal-Organic Frameworks: A Scalable Route to High-Quality Adsorbents.

Metal-Organic Frameworks (MOFs) have been intensively studied for applications such as gas storage, gas separation, catalysis, drug delivery, and more. Typically, the development of MOFs involves a post-synthetic solvent exchange process, which usually requires a significant investment of time, energy, labor, and resources. Herein, we propose a novel post-synthetic processing methodology for commercial and laboratory-scale MOFs called "Suspension Processing." Suspension processing is a non-destructive, agitation-based technique that provides efficient solvent exchange, pore cleaning, and surface defect removal in MOFs. Suspension processing has shown the capability to significantly improve the surface area and gas uptake properties of microporous MOFs, including PCN-250, UiO-66, and HKUST-1. Suspension processing displays improved time, energy, and labor efficiency, as well as considerably enhanced product quality. These findings confirm suspension processing as a straightforward methodology with applicability as a universal technique for the production of high-quality microporous materials.

Incorporating Heavy Alkanes in Metal-Organic Frameworks for Optimizing Adsorbed Natural Gas Capacity.

Metal-organic frameworks (MOFs) as methane adsorbents are highly promising materials for applications such as methane-powered vehicles, flare gas capture, and field natural gas separation. Pre- and post-synthetic modification of MOFs have been known to help improve both the overall methane uptake as well as the working capacity. Here, a post-synthetic modification strategy to non-covalently modify MOF adsorbents for the enhancement of the natural gas uptake for the MOF material is introduced. In this study, PCN-250 adsorbents were doped with C10 alkane and C14 fatty acid and their impact on the methane uptake capabilities was investigated. It was found that even trace amounts of heavy hydrocarbons could considerably enhance the raw methane uptake of the MOF while still being regenerable. The doped hydrocarbons are presumably located at the mesoporous defects of PCN-250, thus optimizing the framework-methane interactions. These findings reveal a general approach that can be used to modify the MOF absorbents, improving their ability to be sustainable and renewable natural gas adsorption platforms.

Machine learning for predicting product distributions in catalytic regioselective reactions.

Gaining predictable control over various forms of selectivities, such as enantio- and/or regio-selectivities, has been a long-standing goal in chemical catalysis. Although a number of factors such as the molecular features of the reactants and catalysts, as well as the reaction conditions, can influence the outcome of a reaction, it is not quite conspicuous as to what combinations of these parameters would offer a desired form of selectivity. We use machine learning tools, such as the neural network (NN), decision tree (DT), logistic regression (LR) and Random forest algorithms, to (a) analyze the outcome of an important catalytic regio-selective difluorination reaction of alkenes, and (b) decipher the complex interplay of various molecular parameters and their non-linear dependencies. The connection between what features of alkenes will yield 1,1-difluorination and how subtle changes would steer the reaction to 1,2-difluorination under identical conditions is enunciated. The NN was able to accurately predict whether a given alkene would yield a 1,1- or 1,2-difluorinated product. A combination of DT and the random forest classifier offered important chemical insights, which could be used in making a more rational choice of the reactant alkene for the desired regioisomeric product. The results could have far reaching implications in predicting which regioisomer is likely to be formed under a given set of conditions, and thus this technique is capable of expediting the development of catalytic transformations.

Formation of a Highly Reactive Cobalt Nanocluster Crystal within a Highly Negatively Charged Porous Coordination Cage.

Earth-abundant first-row transition-metal nanoclusters (NCs) have been extensively investigated as catalysts. However, their catalytic activity is relatively low compared with noble metal NCs. Enhanced catalytic activity of cobalt NCs can be achieved by encapsulating Co NCs in soluble porous coordination cages (PCCs). Two cages, PCC-2a and 2b, possess almost identical cavity in shape and size, while PCC-2a has five times more net charges than PCC-2b. Co2+ cations were accumulated in PCC-2a and reduced to ultra-small Co NCs in situ, while for PCC-2b, only bulky Co particles were formed. As a result, Co NCs@PCC-2a accomplished the highest catalytic activity in the hydrolysis of ammonium borane among all the first-row transition-metals NCs. Based on these results, it is envisioned that confining in the charged porous coordination cage could be a novel route for the synthesis of ultra-small NCs with extraordinary properties.