Unsupervised Clustering of DNA Transmission Footprints Using MoS/WSe Heterojunction.

Quantum transport-based DNA sequencing is emerging as a promising technique in genetic analysis, offering fast, precise, and scalable decoding of genetic information, holding significant potential for applications in human biology and personalized medicine. Given the recent developments in supervised machine learning-coupled nanopore and nanochannel technology, predicting and classifying the labeled DNA nucleotides is now feasible with precision and accuracy. However, the next challenge arises as conventional analysis methods struggle to handle the vast amount of data generated by high-throughput DNA sequencing, particularly when dealing with complex spatial patterns in quantum transport readouts.

Quantum Transport Informed Machine Learning Mapping of Current-Voltage Characteristics for Precision Deoxyribonucleic Acid Sequencing.

Quantum tunneling-based DNA sequencing promises to transform genomic analysis by improving long-read accuracy and enabling high-throughput sequencing, particularly the precise measurement of electrical conductance and tunneling current signatures associated with individual nucleotides. However, key obstacles remain in achieving swift and precise nucleotide identification, such as variation in molecular conductance, noise interference in tunneling current signals, and the complexity of overlapping signal patterns. Here, we employed a quantum transport approach combined with a supervised machine learning (ML) model to accurately classify DNA molecules based on their transmission, conductance, and current readouts, emphasizing their relevance for single-molecule DNA sequencing.

Design, Synthesis, and Cytotoxic Evaluation of New Structurally Simplified and Highly Potent Third-Generation Tubulysin Derivatives.

Tubulysins belong to a class of natural products originally isolated from myxobacteria culture and are known to induce cell apoptosis through inhibition of microtubule assembly. Herein, we report the computationally designed, structurally simplified, and first solid-phase peptide synthesis of novel third-generation tubulin inhibitors in high yields. These inhibitors are devoid of tubuvaline and tubuphenylalanine fragments previously considered essential for tubulin inhibition activity.

Machine Learning Boosted Quantum-Profiling of Blood Antigens.

The molecular-level characterization of glycans, a challenging yet highly desirable goal, is crucial for the comprehensive advancement of glycosciences. Despite significant advances in analytical techniques, including NMR and mass spectrometry, structural and configurational complexity hinders the ability to identify carbohydrates, especially high-order saccharides with regioisomeric glycosidic linkages. In this article, we present a computational methodology that utilizes a quantum tunneling method coupled with machine learning (ML) to recognize a wide range of blood antigens simultaneously.

A hybrid supervised and unsupervised machine learning approach for identifying nucleoside drugs using nanopore readouts.

Nucleoside drugs, mimics of natural nucleosides, have become cornerstone treatments in clinical approaches to combat cancer and viral infections. The analysis of nucleoside drugs is commonly performed using liquid chromatography-tandem mass spectrometry (LC-MS/MS), which requires bulky, expensive instruments and is time-consuming. Notably, while detection methods for natural nucleosides have advanced to their 'next generation' with commercialization, the analysis of nucleoside drugs continues to depend on complex analytical tools.

Machine Learning-Guided Discovery of Alloy Nanoclusters: Steering Morphology-Based Activity and Selectivity Relationships in Bifunctional Electrocatalysts.

Nanoclusters designed with atomic precision are poised to transform next-generation electrode materials for energy devices due to their exceptional performance. However, traditional computational studies often focus solely on individual nanoclusters, neglecting the impact of structurally diverse, low-energy isomers that coexist in a sample. Herein, we present a data-driven approach to screen late-transition metal-based core-shell nanoclusters for bifunctional electrocatalysis.

Unraveling the Hidden Pathway of Catalyst-Free Direct Photochemical Conversion of Sulfides to Sulfoxides: A Universal Pathway under UVA Radiation.

Selective conversion of sulfides to sulfoxides is an important class of chemical transformation that has enormous potential in medicinal chemistry. However, the primary process associated with photoexcitation during the direct photochemical conversion of sulfides to sulfoxides is poorly understood and misrepresented in the literature. Herein, we discover a hidden pathway responsible for the direct photochemical conversion of sulfides to sulfoxides in the absence of any catalysts under UVA illumination (λ = 370 nm).

Cation-Assisted Water Oxidation with Crown Ether-Based Covalent Organic Frameworks.

Covalent organic framework (COF)-based electrocatalysts for water oxidation are usually designed by inducing polarization into an organic framework. The most common methodology "heteroatom insertion" suffers from lower catalytic activity than metalated systems. Herein, we introduce a strategy of polarity induction into organic frameworks by complexing electrolyte's alkali metal using crown ether.

Solvent-Modulated Luminescent Spheroidal Assemblies of Cu Nanocluster for Volatile Amine Sensing.

Cluster-assembled luminescent microstructures built with metal nanoclusters (NCs) represent a promising class of nanomaterials with diverse applications in photonics and sensing. In this work, we have designed a strategy to make a photoluminescent material by assembling atom-precise NCs of [Cu(TFMPT)(DPPE)] (abbreviated as Cu), where TFMPT is 4-hydroxy-6-(trifluoromethyl) pyrimidine-2-thiolate and DPPE is 1,2-bis(diphenylphosphino)ethane. Single-crystal X-ray diffraction (SC-XRD) reveals a unique tetracapped tetrahedral Cu core structure.

Exploring Combined Stereochemically Active Lone-Pair and Rattling Effects in Thermoelectrics with Machine Learning Potentials.

Stereochemically active lone pairs (SCALPs) are recognized for their ability to break local symmetry, induce lattice anharmonicity, and influence thermoelectric properties. Similarly, rattling atoms influence the thermal conductivity by introducing additional vibrational modes that disrupt phonon transport. SCALPs containing pnictogen chalcogenides alongside rattling atoms pose challenges for calculating thermal transport properties using methods because of their noncentrosymmetric structures.

Enhancement of NIR II Emission of AuAg Clusters by Tuning the Au Positions.

Precise alloying at the preferred location to enhance the optical properties of metal nanoclusters is challenging as often the most stable isomer is produced. To alter the location of Au atoms on the [AuAg(SR)] cluster, a new approach by changing the reacting Au precursor following an inter-cluster reaction is reported. A Au(I) containing cluster, [AuSe(DPPE)] as the Au source, while reacting with [Ag(SR)], is used, and the 12 Au atoms occupy the surface position instead of the core.

High Entropy Alloy Formation Derived from High Entropy Oxide: Unlocking the Active Sites for Green Methanol Production from CO.

In pursuit of novel materials for CO conversion to value-added chemicals, previous research has predominantly focused on copper-based, indium oxide (InO)-based, and alloy or intermetallic materials. However, a groundbreaking approach is presented by introducing a high-entropy-based material for CO reduction to methanol (CHOH). This method offers scalability and simplicity, making it feasible for large-scale production of high-entropy-alloys (HEAs).

Machine Learning-Driven Quantum Sequencing of Natural and Chemically Modified DNA.

Simultaneous identification of natural and chemically modified DNA nucleotides at molecular resolution remains a pivotal challenge in genomic science. Despite significant advances in current sequencing technologies, the ability to identify subtle changes in natural and chemically modified nucleotides is hindered by structural and configurational complexity. Given the critical role of nucleobase modifications in data storage and personalized medicine, we propose a computational approach using a graphene nanopore coupled with machine learning (ML) to simultaneously recognize both natural and chemically modified nucleotides, exploring a wide range of modifications in the nucleobase, sugar, and phosphate moieties while investigating quantum transport mechanisms to uncover distinct molecular signatures and detailed electronic and orbital insights of the nucleotides.

Hydrophosphanylation of Alkynes via Magnesium Complexes: Evidence for Ligand Dependency in Structure-Activity Relationships.

Pursuing practical, straightforward, and sustainable methods for forming carbon-phosphorus bonds is crucial in academia and industry. In this study, we showed that bis(diiminate)-based magnesium complexes [L(Mg-nBu)] (nBu = n-butyl) could effectively catalyze the hydrophosphanylation of alkynes, resulting in monophosphanylated vinyledene- and 1,2-diphosphanylated alkanes in a stepwise manner. This transformation showcases an excellent atom economy, broad functional group tolerance, and gram-scale synthesis for organophosphorus compounds.

Deciphering Electrocatalytic Activity in Cu Nanoclusters: Interplay Between Structural Confinement and Ligands Environment.

Ligand-protected copper nanoclusters (Cu NCs) with atomic precision have emerged rapidly due to their fascinating structural architectures and versatile catalytic properties, making them ideal for investigating structure-activity relationships. Despite their potential, challenges such as stability issues and limited structural diversity have restricted deeper exploration. In this study, three distinct Cu NCs are synthesized using a one-pot reduction strategy by carefully modifying reaction conditions.

Harnessing the Synergistic Effects of Ir Co-Decorated NiMn-LDH: a Multi-Analytical Approach to Boost Electrocatalytic Overall Water Splitting Activity in Alkaline Condition.

Fine control over the Ir precursor to the Nickel-based layered double hydroxides (LDHs) is significant for decorating both single atoms (SA) and nanoclusters (NC), thus modulating catalytic kinetics and improving overall performance. In this study, NiMn-LDH is synthesized and co-decorated it with Iridium, introducing a new pathway for developing efficient bifunctional electrocatalysts in water-splitting technologies. Additionally, a typical fibrous material has developed by immobilizing.

Structural Changes in Atomically Precise Ag Nanoclusters upon Sequential Attachment and Detachment of Secondary Ligands.

Elucidating the structural dynamics of ligand-stabilized noble metal nanoclusters (NCs) is critical for understanding their properties and for developing applications. Ligand rearrangement at NC surfaces is an important contributor to structural change. In this study, we investigate the dynamic behavior of ligand-protected [Ag(L)] NC's (L = 1,3-benzenedithiol) interacting with secondary ligand 2,2'-[1,4-phenylenebis (methylidynenitrilo)] bis[benzenethiol] (referred to as ).

Unraveling the Synergistic Role of Sm Doped NiFe-LDH as High-Performance Electrocatalysts for Improved Anion Exchange Membrane and Water Splitting Applications.

Effective first-row transition metal-based electrocatalysts are crucial for large-scale hydrogen energy generation and anion exchange membrane (AEM) devices in water splitting. The present work describes that SmNiFe-LDH nanosheets on nickel foam are used as a bifunctional electrocatalyst for water splitting and AEM water electrolyzer study. Tuning the Ni-to-Fe ratios in NiFe-LDH and doping with Sm ions improves the electrical structure and intrinsic activity.

Metal-Free Electrocatalytic Alkaline Water Splitting by Porous Macrocyclic Proton Sponges.

Macrocycles are unique as they encapsulate and transfer guest molecules or ions and facilitate catalytic processes. Although metalated macrocycles are pivotal in electrocatalytic processes, using metal-free analogs has been rare. Following the strategy of Kanbara et al.

Automated-Screening Oriented Electric Sensing of Vitamin B1 Using a Machine Learning Aided Solid-State Nanopore.

Micronutrient detection and identification at the single-molecule level are paramount for both clinical and home diagnostics. Analytical tools such as high-performance liquid chromatography and liquid chromatography-tandem mass spectrometry have been widely used but include a high instrument cost and prolonged analysis time. Here, as a model system, by merging nanopore signatures with machine learning algorithms, we propose an automated electric sensing strategy to identify vitamin B1 and its phosphorylated derivatives with good accuracy.

Machine Learning-Enhanced Screening of Single-Atom Alloy Clusters for Nitrogen Reduction Reaction.

The electrochemical nitrogen reduction reaction (eNRR) under ambient conditions is a promising method to generate ammonia (NH), a crucial precursor for fertilizers and chemicals, without carbon emissions. Single-atom alloy catalysts (SAACs) have reinvigorated catalytic processes due to their high activity, selectivity, and efficient use of active atoms. Here, we employed density functional theory (DFT) calculations integrated with machine learning (ML) to investigate dodecahedral nanocluster-based SAACs for analyzing structure-activity relationships in eNRR.

Effect of graphene electrode functionalization on machine learning-aided single nucleotide classification.

Solid-state nanogap-based DNA sequencing with a quantum tunneling approach has emerged as a promising avenue due to its potential to deliver swift and precise sequencing outcomes. Nevertheless, despite significant progress, experimentally achieving single base resolution with a high signal-to-noise ratio remains a daunting challenge. In this work, we have utilized a machine learning (ML) framework coupled with the quantum transport method to assess and compare the nucleotide identification performance of graphene nanogaps functionalized with four different edge-saturating entities (C, H, N, and OH).