Charge-Transfer Knowledge Graph among Amino Acids Derived from High-Throughput Electronic Structure Calculations for Protein Database

Electrochemical detection of acute renal disease biomarker by Galinstan nanoparticles interfaced to bilayer polymeric structured dirhenium heptoxide film

This work describes a facile fabrication of an efficient electrochemical sensor utilizing sonication-derived Galinstan nanoparticles (Galinstan NPs) interfaced to annealed dirhenium heptoxide (Re₂O₇) thin-film on Silicon (Si) for the quantitative detection of the most promising acute renal disease biomarker Neutrophil Gelatinase Associated Lipocalin (NGAL). Under optimized preconditions, the anti-NGAL antibodies were immobilized on the Galinstan NPs/Re₂O₇/Si electrode by carbodiimide crosslinking to detect NGAL. The composition, morphology, and structural properties of the electrode were elucidated by various physical characterizations. The sensor obtained a high sensitivity (0.018 µA^-1ng^-1ml^-1, R² = 0.99) in differential pulse voltammetry and a minimum detection limit (2.14 ng ml^-1) in electrochemical impedance spectroscopy for a wide range of NGAL concentrations (25-650 ng ml^-1) with high selectivity and stability. The intensified performance of the sensor was achieved by the summed-up electron transfer from the Re₂O₇ film to Galinstan NPs and Galinstan NPs to the electroactive reactants. Additionally, the outer 2D gallium oxide (Ga₂O₃) layer of Galinstan Nps enhanced the redox activities, whereas the metallic core contributed to the magnificent conductivity. The excellent recovery rates of the sensor for different concentrations of NGAL measured in commercial human serum by the standard addition method assured the feasibility of the sensor.

Molecular electronics behaviour of L-aspartic acid using symmetrical metal electrodes

Protein-based electronics is one of the growing areas of bio-nanoelectronics, where novel electronic devices possessing distinctive properties are being fabricated using specific proteins. Furthermore, if the bio-molecule is analysed amidst different electrodes, intriguing properties are elucidated. This research article investigates the electron transport properties of L-aspartic acid (i.e. L-amino acid) bound to symmetrical electrodes of gold, silver, copper, platinum and palladium employing NEGF-DFT approach using self-consistent function. The theoretical work function of different electrodes is calculated using local density approximation and generalized gradient approximation approach. The calculated work function correlates well with the hole tunneling barrier and conductance of the molecular device, which further authenticate the coupling strength between molecule and electrode. Molecule under consideration also exhibits negative differential resistance region and rectification ratio with all the different electrodes, due to its asymmetrical structure. The molecular device using platinum electrodes exhibits the highest peak to valley ratio of 1.38 and rectification ratio of 3.20, at finite bias. The switching characteristics of different molecular device are justified with detailed transmission spectra and MPSH. These results indicate that L-aspartic acid and similar biomolecule can be vital to the growth of Proteotronics.

Molecular fragmentation as a way to reveal early electron dynamics induced by attosecond pulses

We present a theoretical study of the electron and nuclear dynamics that would arise in an attosecond two-color XUV-pump/XUV-probe experiment in glycine. In this scheme, the broadband pump pulse suddenly ionizes the molecule and creates an electronic wave packet that subsequently evolves under the influence of the nuclear motion until it is finally probed by the second XUV pulse. To describe the different steps of such an experiment, we have combined a multi-reference static-exchange scattering method with a trajectory surface hopping approach. We show that by changing the central frequency of the pump pulse, i.e., by engineering the initial electronic wave packet with the pump pulse, one can drive the cation dynamics into a specific fragmentation pathway. Reminiscence of this early electron dynamics can be observed in specific fragmentation channels (not all of them) as a function of the pump-probe delay and in time-resolved photoelectron spectra at specific photoelectron energies. The optimum conditions to visualize the initial electronic coherence in photoelectron and photo-ion spectra depend very much on the characteristics of the pump pulse as well as on the electronic structure of the molecule under study.

A Fluidics-Based Biosensor to Detect and Characterize Inhibition Patterns of Organophosphate to Acetylcholinesterase in Food Materials

A chip-based electrochemical biosensor is developed herein for the detection of organophosphate (OP) in food materials. The principle of the sensing platform is based on the inhibition of dimethoate (DMT), a typical OP that specifically inhibits acetylcholinesterase (AChE) activity. Carbon nanotube-modified gold electrodes functionalized with polydiallyldimethylammonium chloride (PDDA) and oxidized nanocellulose (NC) were investigated for the sensing of OP, yielding high sensitivity. Compared with noncovalent adsorption and deposition in bovine serum albumin, bioconjugation with lysine side chain activation allowed the enzyme to be stable over three weeks at room temperature. The total amount of AChE was quantified, whose activity inhibition was highly linear with respect to DMT concentration. Increased incubation times and/or DMT concentration decreased current flow. The composite electrode showed a sensitivity 4.8-times higher than that of the bare gold electrode. The biosensor was challenged with organophosphate-spiked food samples and showed a limit of detection (LOD) of DMT at 4.1 nM, with a limit of quantification (LOQ) at 12.6 nM, in the linear range of 10 nM to 1000 nM. Such performance infers significant potential for the use of this system in the detection of organophosphates in real samples.

Quantum Chemistry in the Age of Machine Learning

As the quantum chemistry (QC) community embraces machine learning (ML), the number of new methods and applications based on the combination of QC and ML is surging. In this Perspective, a view of the current state of affairs in this new and exciting research field is offered, challenges of using machine learning in quantum chemistry applications are described, and potential future developments are outlined. Specifically, examples of how machine learning is used to improve the accuracy and accelerate quantum chemical research are shown. Generalization and classification of existing techniques are provided to ease the navigation in the sea of literature and to guide researchers entering the field. The emphasis of this Perspective is on supervised machine learning.