Machine Learning Recognition of Artificial DNA Sequence with Quantum Tunneling Nanogap Junction.

Artificially synthesized DNA holds significant promise in addressing fundamental biochemical questions and driving advancements in biotechnology, genetics, and DNA digital data storage. Rapid and precise electric identification of these artificial DNA strands is crucial for their effective application. Herein, we present a comprehensive investigation into the electric recognition of eight artificial synthesized DNA (DNA and DNA) nucleobases using quantum tunneling transport and machine learning (ML) techniques.

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.

Dehydrogenative Coupling for Synthesis of Quinazolin-4(3H)-ones via Tandem Reaction using Ruthenium(II)-Phenyl-Azo-Naphthaldoxime: An Experimental and Theoretical Investigation.

The bidentate N, N, donor phenyl-azo-naphthaldoxime NpLH, 1 was used to synthesize the ruthenium(II) complex trans-[Ru(NpL)(CO)Cl(PPh)], 2. It has been characterized by SCXRD, electrochemical and spectral studies. Computational analysis indicates that the low-lying π*-LUMO of the complex has substantial azo-character of coordinated ligand.

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.

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).

Integration of Artificial Intelligence and Quantum Transport toward Stereoselective Identification of Carbohydrate Isomers.

Detection of stereoisomers of carbohydrates with molecular resolution, a challenging goal analysts desire to achieve, is key to the full development of glycosciences. Despite the promise that analytical techniques made, including widely used nuclear magnetic resonance and mass spectrometry, high throughput carbohydrate sequencing remains an unsolved issue. Notably, while next-generation sequencing technologies are readily available for DNA and proteins, they are conspicuously absent for carbohydrates due to the immense stereochemical and structural complexity inherent in these molecules.

A lignin-derived carbon dot-upgraded bacterial cellulose membrane as an all-in-one interfacial evaporator for solar-driven water purification.

Solar-driven interfacial evaporation has emerged as an efficient approach for wastewater treatment and seawater desalination. New trends demand adaptive technology to develop photothermal membranes with multifunctional features. Herein, we report a robust multi-purpose near-infrared (NIR)-active hydrogel composite (-BC@N-LCD) from broad-spectrum active nitrogen-doped lignin-derived carbon dots (N-LCDs) covalently cross-linked with a bacterial cellulose (BC) matrix.

Machine learning empowered next generation DNA sequencing: perspective and prospectus.

The pursuit of ultra-rapid, cost-effective, and accurate DNA sequencing is a highly sought after aspect of personalized medicine development. With recent advancements, mainstream machine learning (ML) algorithms hold immense promise for high throughput DNA sequencing at the single nucleotide level. While ML has revolutionized multiple domains of nanoscience and nanotechnology, its implementation in DNA sequencing is still in its preliminary stages.

Biomolecular Condensation of Trypsin Prevents Autolysis and Promotes Ca-Mediated Activation of Esterase Activity.

The presence of Ca ions is known to facilitate the activity of trypsin-like serine proteases via structural stabilization against thermal denaturation and autolysis. Herein, we report a new and hidden regulatory role of Ca in the catalytic pathways of trypsin and α-chymotrypsin under physiological conditions. We discovered that macromolecular crowding promotes spontaneous homotypic condensation of trypsin via liquid-liquid phase separation to yield membraneless condensates over a broad range of concentrations, pH, and temperature, which are stabilized by multivalent hydrophobic interactions.

Integration of Density Functional Theory and Machine Learning for Electrolyte Optimization in High-Voltage Dual-Ion Battery Design.

Designing dual-ion batteries (DIBs) by using various electrolytes through experiments or computationally is highly time-consuming and needs high-cost sophisticated resources. Here, we have utilized the ultrafast screening capability of machine learning (ML) to search for suitable salt-electrolytes toward the design of DIBs, choosing voltage as the desirable descriptor. Considering 50 different salts and their suitable staging mechanisms, the XGBoost Regressor ML model has been found to perform with remarkable accuracy.

Integrated Supervised and Unsupervised Machine Learning Approach to Map the Electrochemical Windows Over 4500 Solvents for Battery Applications.

The compatibility between solvent electrolytes and high-voltage electrode materials represents a significant impediment to the progress of rechargeable metal-ion batteries. Rapidly identifying suitable solvent electrolytes with optimized electrochemical windows (ECWs) within an extensive search space poses a formidable challenge. In this study, we introduce a combined supervised and unsupervised (clustering) machine learning (ML) approach to discern distinct clusters of solvent electrolytes exhibiting varying ECW ranges.

Comparison of lattice thermal conductivity usingDFT, machine learning interatomic potentials, and temperature dependent effective potential: a case study of hexagonal BN and BP bilayer.

Discovering high thermal conductivity materials is essential for various practical applications, particularly in electronic cooling. The significance of two-dimensional (2D) materials lies in their unique properties that emerge due to their reduced dimensionality, making them highly promising for a wide range of applications. Hexagonal boron nitride (BN), both monolayer and bilayer forms, has garnered attention for its fascinating properties.

Machine Learning-Assisted Direct RNA Sequencing with Epigenetic RNA Modification Detection via Quantum Tunneling.

RNA sequence information holds immense potential as a drug target for diagnosing various RNA-mediated diseases and viral/bacterial infections. Massively parallel complementary DNA (c-DNA) sequencing helps but results in a loss of valuable information about RNA modifications, which are often associated with cancer evolution. Herein, we report machine learning (ML)-assisted high throughput RNA sequencing with inherent RNA modification detection using a single-molecule, long-read, and label-free quantum tunneling approach.

Secure hardware IP of GLRT cascade using color interval graph based embedded fingerprint for ECG detector.

This paper presents a security aware design methodology to design secure generalized likelihood ratio test (GLRT) hardware intellectual property (IP) core for electrocardiogram (ECG) detector against IP piracy and fraudulent claim of IP ownership threats. Integrating authentic (secure version) GLRT hardware IP core in the system-on-chip (SoC) of ECG detectors is paramount for reliable operation and estimation of ECG parametric data, such as Q wave, R wave and S wave (QRS) complex detection. A pirated GLRT hardware IP integrated into an ECG detector may result in an unreliable/erratic estimation of ECG parametric data that can be hazardous and fatal for the end patient.

Precision Basecalling of Single DNA Nucleotide from Overlapped Transmission Readouts with Machine Learning Aided Solid-State Nanogap.

DNA sequencing with the quantum tunneling technique heralds a paradigm shift in genetic analysis, promising rapid and accurate identification for diverging applications ranging from personalized medicine to security issues. However, the widespread distribution of molecular conductance, conduction orbital alignment for resonant transport, and decoding crisscrossing conductance signals of isomorphic nucleotides have been persistent experimental hurdles for swift and precise identification. Herein, we have reported a machine learning (ML)-driven quantum tunneling study with solid-state model nanogap to determine nucleotides at single-base resolution.

Advancement of Next-Generation DNA Sequencing through Ionic Blockade and Transverse Tunneling Current Methods.

DNA sequencing is transforming the field of medical diagnostics and personalized medicine development by providing a pool of genetic information. Recent advancements have propelled solid-state material-based sequencing into the forefront as a promising next-generation sequencing (NGS) technology, offering amplification-free, cost-effective, and high-throughput DNA analysis. Consequently, a comprehensive framework for diverse sequencing methodologies and a cross-sectional understanding with meticulous documentation of the latest advancements is of timely need.

Insights of the efficient hydrogen evolution reaction performance in bimetallic AuCu nanoclusters.

The design of efficient electrocatalysts for improving hydrogen evolution reaction (HER) performance using atomically precise metal nanoclusters (NCs) is an emerging area of research. Here, we have studied the HER electrocatalytic performance of monometallic Cu and Au nanoclusters and bimetallic AuCu nanoclusters. A bimetallic AuCu/MoS composite exhibits excellent HER catalytic activity with an overpotential () of 155 mV reversible hydrogen electrode observed at 10 mA cm current density.

growth of a redox-active metal-organic framework on electrospun carbon nanofibers as a free-standing electrode for flexible energy storage devices.

The rational construction of free-standing and flexible electrodes for application in electrochemical energy storage devices and next-generation supercapacitors is an emerging research focus. Herein, we prepared a redox-active ferrocene dicarboxylic acid (Fc)-based nickel metal-organic framework (MOF) on electrospun carbon nanofibers (NiFc-MOF@CNFs) an approach. This approach avoided the aggregation of the MOF.

Photoluminescence Properties of a Chiral One-Dimensional Silver Chalcogenolate Chain.

Atom-precise metal nanoclusters, which contain a few tens to hundreds of atoms, have drawn significant interest due to their interesting physicochemical properties. Structural analysis reveals a fundamental architecture characterized by a central core or kernel linked to a staple motif with metal-ligand bonding playing a pivotal role. Ligands not only protect the surface but also exert a significant influence in determining the overall assembly of the larger superstructures.

Unlocking the Potential of Dual-Ion Batteries: Identifying Polycyclic Aromatic Hydrocarbon Cathodes and Intercalating Salt Combinations through Machine Learning.

Dual-ion batteries (DIBs) represent a promising energy storage technology, offering a cost-effective safe solution with impressive electrochemical performance. The large combinatorial configuration space of the electrode-electrolyte leads to design challenges. We present a machine learning (ML) approach for accurately predicting the voltage and volume changes of polycyclic aromatic hydrocarbon (PAH) cathodes upon intercalation with a variety of DIB salts following different mechanisms.

Deciphering DNA nucleotide sequences and their rotation dynamics with interpretable machine learning integrated CN nanopores.

A solid-state nanopore combined with the quantum transport method has garnered substantial attention and intrigue for DNA sequencing due to its potential for providing rapid and accurate sequencing results, which could have numerous applications in disease diagnosis and personalized medicine. However, the intricate and multifaceted nature of the experimental protocol poses a formidable challenge in attaining precise single nucleotide analysis. Here, we report a machine learning (ML) framework combined with the quantum transport method to accelerate high-throughput single nucleotide recognition with CN nanopores.

All optical modulation in vertically coupled indium tin oxide ring resonator employing epsilon near zero state.

We present an innovative approach to achieve all-optical modulation within an ITO-based vertically coupled ring resonator. This method leverages the material's enhanced nonlinear response in the near-infrared wavelengths, particularly within the epsilon-near-zero (ENZ) state. To enhance the interaction between light and the material while minimizing scattering losses, our approach employs an ITO-based vertically connected ring resonator.

Protein Sequencing with Artificial Intelligence: Machine Learning Integrated Phosphorene Nanoslit.

Achieving high throughput protein sequencing at single molecule resolution remains a daunting challenge. Herein, relying on a solid-state 2D phosphorene nanoslit device, an extraordinary biosensor to rapidly identify the key signatures of all twenty amino acids using an interpretable machine learning (ML) model is reported. The XGBoost regression algorithm allows the determination of the transmission function of all twenty amino acids with high accuracy.

Macromolecular Crowding Promotes Re-entrant Liquid-Liquid Phase Separation of Human Serum Transferrin and Prevents Surface-Induced Fibrillation.

Protein aggregation and inactivation upon surface immobilization are major limiting factors for analytical applications in biotechnology-related fields. Protein immobilization on solid surfaces often requires multi-step surface passivation, which is time-consuming and inefficient. Herein, we have discovered that biomolecular condensates of biologically active human serum transferrin (Tf) can effectively prevent surface-induced fibrillation and preserve the native-like conformation of phase-separated Tf over a period of 30 days.

Coacervate-Based Plexcitonic Assembly toward Peroxidase-like Activity and Ultrasensitive Glucose Sensing.

Inbuilt catalytic centers anchored inside the confined architecture of artificial nanoreactors have gained tremendous attention owing to their vast applicability in various catalytic transformations. However, designing homogeneously distributed catalytic units with exposed surfaces in confined environment is a challenging task. Here, we have utilized quantum dot (QD)-embedded coacervate droplets (QD-Ds) as a confined compartment for the in situ synthesis of gold nanoparticles (Au NPs) without any additional reducing agent.