Semi-empirical Approach Research Articles

CuO nanoparticles passivated 2D MXene-based voltammetric sensor for detecting environmental hazardous pollutant

This research explores a novel method for electrochemical sensing of the organic pollutant diclofenac sodium. Glassy carbon electrodes (GCEs) were modified with ultrasonically prepared MXene/CuO nanoparticle composites, demonstrating a wide linear detection range of 0.34 µM–31.67 µM for the contaminant. The study involved synthesizing 2D MXene via HF etching and DMSO delamination, and CuO nanoparticles through a hydrothermal process. Powder XRD, FE-SEM, EDAX, HR-TEM, and SAED techniques characterized the synthesized materials and composites. Cyclic voltammetry (CV), differential pulse voltammetry (DPV), square wave voltammetry (SWV), and electrochemical impedance spectroscopy (EIS) were employed to analyze the electrochemical behaviour of the nanocomposites. Furthermore, the binding interaction between the MXene/CuO composite and diclofenac sodium was investigated using molecular modelling and semi-empirical approaches. The results revealed that CuO nanoparticle incorporation significantly enhances the sensitivity of MXene towards diclofenac. This novel sensor exhibited satisfactory performance, suggesting its potential application as a robust platform for environmental monitoring systems.

Comparative Evaluation of Semi-Empirical Approaches to Retrieve Satellite-Derived Chlorophyll-a Concentrations from Nearshore and Offshore Waters of a Large Lake (Lake Ontario)

Chlorophyll-a concentration (Chl-a) is commonly used as a proxy for phytoplankton abundance in surface waters of large lakes. Mapping spatial and temporal Chl-a distributions derived from multispectral satellite data is therefore increasingly popular for monitoring trends in trophic state of these important ecosystems. We evaluated products of eleven atmospheric correction processors (LEDAPS, LaSRC, Sen2Cor, ACOLITE, ATCOR, C2RCC, DOS 1, FLAASH, iCOR, Polymer, and QUAC) and 27 reflectance indexes (including band-ratio, three-band, and four-band algorithms) recommended for Chl-a concentration retrieval. These were applied to the western basin of Lake Ontario by pairing 236 satellite scenes from Landsat 5, 7, 8, and Sentinel-2 acquired between 2000 and 2022 to 600 near-synchronous and co-located in situ-measured Chl-a concentrations. The in situ data were categorized based on location, seasonality, and Carlson’s Trophic State Index (TSI). Linear regression Chl-a models were calibrated for each processing scheme plus data category. The models were compared using a range of performance metrics. Categorization of data based on trophic state yielded improved outcomes. Furthermore, Sentinel-2 and Landsat 8 data provided the best results, while Landsat 5 and 7 underperformed. A total of 28 Chl-a models were developed across the different data categorization schemes, with RMSEs ranging from 1.1 to 14.1 μg/L. ACOLITE-corrected images paired with the blue-to-green band ratio emerged as the generally best performing scheme. However, model performance was dependent on the data filtration practices and varied between satellites.

Argentophilic Interactions, Flexibility, and Dynamics of Pyrrole Cages Encapsulating Silver(I) Clusters.

Recently, pyrrole cages have been synthesized that encapsulate ion pairs and silver(I) clusters to form intricate supramolecular capsules. We report here a computational analysis of these structures using density functional theory combined with a semiempirical tight-binding approach. We find that for neutral pyrrole cages, the Gibbs free energies of formation provide reliable predictions for the ratio of bound ions. For charged pyrrole cages, we find strong argentophilic interactions between Ag ions on the basis of the calculated bond indices and molecular orbitals. For the cage with the Ag4 cluster, we find two minimum-geometry conformations that differ by only 6.5 kcal/mol, with an energy barrier <1 kcal/mol, suggesting a very flexible structure as indicated by molecular dynamics. The predicted energies of formation of [Agn⊂1]n-3+ (n = 1-5) cryptands provide low energy barriers of formation of 5-20 kcal/mol for all cases, which is consistent with the experimental data. Furthermore, we also examined the structural variability of mixed-valence silver clusters to test whether additional geometrical conformations inside the organic cage are thermodynamically accessible. In this context, we show that the time-dependent density functional theory UV-vis spectra may potentially serve as a diagnostic probe to characterize mixed-valence and geometrical configurations of silver clusters encapsulated into cryptands.

Theoretical Exploration of Structural and Excitonic Properties in Black Phosphorus: From First-Principles to a Semi-Empirical Approach

Theoretical Exploration of Structural and Excitonic Properties in Black Phosphorus: From First-Principles to a Semi-Empirical Approach

Soil suction effects on CPT data interpretation

The Cone Penetration Test (CPT) is one of the most widely used in situ test for site characterization and estimation of geotechnical parameters estimative. Its benefits include speed, reliability, repeatability, and the ability to provide continuous data. Although the interpretation is well-established for saturated and dry soils, it remains limited for unsaturated soils. This paper presents and discusses the influence of the unsaturated condition in CPTs performed on tropical soil site. Four CPT campaigns and gravimetric water content profiles have been determined in different periods over two years. Soil-water retention curves (SWRC) were used to estimate in situ soil suction. It was observed that the CPT data were influenced by soil suction up to 6.0 m depth. Two semi-empirical approaches based on bearing capacity theory and effective stress principle were used for data interpretation. These approaches allowed to assess the soil suction influence on CPT and to define the typical test profile with no suction effects. The importance of considering soil suction in the CPT interpretation on unsaturated soils is highlighted, and the effective stress approach is suggested as a starting point.

Prediction Challenge: Simulating Rydberg photoexcited cyclobutanone with surface hopping dynamics based on different electronic structure methods.

This research examines the nonadiabatic dynamics of cyclobutanone after excitation into the n → 3s Rydberg S2 state. It stems from our contribution to the Special Topic of the Journal of Chemical Physics to test the predictive capability of computational chemistry against unseen experimental data. Decoherence-corrected fewest-switches surface hopping was used to simulate nonadiabatic dynamics with full and approximated nonadiabatic couplings. Several simulation sets were computed with different electronic structure methods, including a multiconfigurational wavefunction [multiconfigurational self-consistent field (MCSCF)] specially built to describe dissociative channels, multireference semiempirical approach, time-dependent density functional theory, algebraic diagrammatic construction, and coupled cluster. MCSCF dynamics predicts a slow deactivation of the S2 state (10ps), followed by an ultrafast population transfer from S1 to S0 (<100fs). CO elimination (C3 channel) dominates over C2H4 formation (C2 channel). These findings radically differ from the other methods, which predicted S2 lifetimes 10-250 times shorter and C2 channel predominance. These results suggest that routine electronic structure methods may hold low predictive power for the outcome of nonadiabatic dynamics.

Determination of the Hammett Acidity of HF/Base Reagents.

Harnessing the acidity of HF/base reagents is of paramount importance to improve the efficiency and selectivity of fluorination reactions. Yet, no general method has been reported to evaluate their acidic properties, and experimental designs are still relying on a trial-and-error approach. We report a new method based on 19F NMR spectroscopy which allows highly sensitive measures and short-time analyses. Advantageously, the basic properties of the indicators can be determined upstream by DFT calculations, affording a simple yet robust semiempirical approach. In particular, the indicators used in this study were rationally designed to fit on the conceptually appealing and commonly used Hammett scale. This method has been applied to commercially available and recently developed HF/base reagents.

An Update in Computational Methods for Environmental Monitoring: Theoretical Evaluation of the Molecular and Electronic Structures of Natural Pigment-Metal Complexes.

Metals are beneficial to life, but the presence of these elements in excessive amounts can harm both organisms and the environment; therefore, detecting the presence of metals is essential. Currently, metal detection methods employ powerful instrumental techniques that require a lot of time and money. Hence, the development of efficient and effective metal indicators is essential. Several synthetic metal detectors have been made, but due to their risk of harm, the use of natural pigments is considered a potential alternative. Experiments are needed for their development, but they are expensive and time-consuming. This review explores various computational methods and approaches that can be used to investigate metal-pigment interactions because choosing the right methods and approaches will affect the reliability of the results. The results show that quantum mechanical methods (ab initio, density functional theory, and semiempirical approaches) and molecular dynamics simulations have been used. Among the available methods, the density functional theory approach with the B3LYP functional and the LANL2DZ ECP and basis set is the most promising combination due to its good accuracy and cost-effectiveness. Various experimental studies were also in good agreement with the results of computational methods. However, deeper analysis still needs to be carried out to find the best combination of functions and basis sets.

Terramechanics Models for Tracked Vehicle Terrain Interaction Analysis A Review

Efficient manoeuvrability of off-road tracked vehicles such as military tanks and rovers is essential in ensuring the success of military/extra-terrestrial operations. To achieve this, in-depth research on vehicle-terrain interaction is crucial. This manuscript deals with reviewing the ways to study terramechanics viz. theoretical, empirical, and field tests, and proposing the merits and demerits of each method. Under the theoretical approach, empirical, numerical, and semi-empirical methods are discussed. Under the empirical approach, the method based onthe vehicle cone index for tracked and wheeled vehicles is discussed. Under the numerical approach, advantages and disadvantages of Finite Element Method (FEM) and Discrete Element Method (DEM) are discussed. Semi-empirical method, based upon a combination of the best features of numerical and empirical approaches discusses terrain response to normal repetitive loads and shear repetitive loads for tracked as well as wheeled vehicles. Pressure sinkage relationship for terrains at various loading conditions and shear stress displacement relationship for different terrains obtained through penetration and shear tests are discussed to determine the vehicle’s mobility parameters under a semi-empirical approach. Further, the Super element model, multi-body simulation model, and ride and cornering vibration model are discussed under computer simulation models. A detailed review of various models customized towards tracked vehicle-terrain interaction discussed in this manuscript helps the authors set up a laboratory for terramechanics at DIAT. Preliminary analysis along with conceptual design of the experimental setup is also discussed. In a nutshell, this paper attempts to summarize the research that has been carried out in the field of tracked vehicle-terrain inter action comprising of VCI, MMP, FEM, DEM, Super element model, Multibody technique, and Semi-empirical methods helping the authors to establish a laboratory of terramechanics for their M. Tech. program on Armament and Combat Vehicles at DIAT Pune.

A Compact Model of Carbon Nanotube Field-Effect Transistors for Various Sizes with Bipolar Characteristics

Carbon nanotubes have excellent electrical properties and can be used as a new generation of semiconductor materials. This paper presents a compact model for carbon nanotube field-effect transistors (CNTFETs). The model uses a semi-empirical approach to model the current–voltage properties of CNTFETs with gate lengths exceeding 100 nm. This study introduces an innovative approach by proposing physical parametric reference lengths (Lref), which facilitate the integration of devices of varying sizes into a unified modeling framework. Furthermore, this paper develops models for the bipolar properties of carbon nanotube devices, employing two distinct sets of model parameters for enhanced accuracy. The model offers a comprehensive analysis of the different capacitances occurring between the electrodes within the device. The simulation of the model shows good agreement with the experimental measurements, confirming the model’s validity. The model is implemented in the Verilog-A hardware description language, with the circuit being subsequently constructed and subjected to simulations via the HSPICE tool. The CNTFET-based inverter exhibits a gain of 7.022 and a delay time of 16.23 ps when operated at a voltage of 1.2 V.

Methodology to assess scour development around complex structures

Accurate prediction of scour evolution around fixed offshore foundations is a key parameter for selecting the most appropriate scour mitigation strategy. Various formulations can be used to estimate scour around monopiles. However, non-specific formulations have been developed for more complex foundations. In the case of jacketed offshore substations (OSS), the geometry can be one of the most complex. This paper describes a hybrid methodology (numerical model simulations, semi-empirical approaches and physical experiments) to estimate the scour evolution around OSS. The application of this methodology allows the selection of the most appropriate mitigation strategy from an early stage of the project to the final decision. The methodology is accompanied by some examples. In general, the results obtained from the semi-empirical approaches show quite good agreement with the measurements from the physical experiments. Nevertheless, considering the complex geometry of the OSS and the potential non-linearities associated with the platform geometry, physical experiments are required to ensure the correct estimation of the maximum scour (local and global).

Binary and ternary approach of solubility of Rivaroxaban for preparation of developed nano drug using supercritical fluid

This study addressed the solubility of Rivaroxaban in supercritical carbon dioxide at a temperature range of 308–338 K and a pressure range of 12–30 MPa with and without a Co-solvent in binary and ternary systems. The impact of ethanol Co-solvent was also examined. Furthermore, the examined systems were modeled using semi-empirical approaches once the tentative solubility data were determined. Rivaroxaban solubility in the binary and ternary systems ranged based on mole fraction from 1.0×10-6 to 2.57×10-5 and 1.9×10-5 to 2.02×10-4, respectively. Based on the results, the use of a Co-solvent can greatly boost the solubility of Rivaroxaban. The highest Co-solvent effect on the Rivaroxaban-Ethanol-CO2 mixture was observed at 18.73 (338 K and 12 MPa). Furthermore, empirical and semi-empirical models can effectively fit the solubility values of the analyzed materials by AARD% and Radj for binary and ternary approaches. The Jouyban et al. (AARD%=7.40 and Radj = 0.993) model for the binary system and the Garlapati-Madras (AARD%=6.16 and Radj = 0.991) and Sodeifian-Sajadian (AARD%=6.13 and Radj = 0.979) and Soltani-Mazloumi (AARD%=6.89 and Radj = 0.987) models for the ternary system are the most accurate models.

Hydrostatic pressure response of semiconducting GaSb applying a semi-empirical approach

In addition to experimental investigations, semi-empirical approaches prove valuable for replicating experimental results and predicting material behavior under high-pressure conditions. This study investigates the variation of unit cell volume in cubic zinc blende Gallium antimonide (GaSb) up to the phase transition pressure, relying solely on structural parameters from literature measurements at ambient conditions. The research extends to inspect the impact of high pressure on various properties, including mass density, bulk modulus, bulk sound wave velocity, microhardness, first-order pressure derivative of the isothermal bulk modulus, Grüneisen parameter, and Debye temperature for the GaSb compound. The bulk modulus and microhardness show nearly linear increases with pressure, while sound wave velocity and Debye temperature exhibit nonlinear variations. The first-order pressure derivative of the bulk modulus and Grüneisen parameter gradually decreased in the material under study. Similar trends under pressure are found in literature for materials with different crystallographic structures. Additionally, the study predicts and analyzes extra physical parameters of GaSb, comparing them with existing literature data.

A Comparative Study of Efficient Modeling Approaches for Performing Controlled-Depth Abrasive Waterjet Pocket Milling

Non-conventional processes are considerably important for the machining of hard-to-cut alloys in various demanding applications. Given that the surface quality and integrity, dimensional accuracy, and productivity are important considerations in industrial practice, the prediction of the outcome of the material removal process should be able to be conducted with sufficient accuracy, taking into consideration the computational cost and difficulty of implementation of the relevant models. In the case of AWJ, various types of approaches have been already proposed, both relying on analytical or empirical models and developed by solving partial differential equations. As the creation of a model for AWJ pocket milling is rather demanding, given the number of parameters involved, in the present work, it is intended to compare the use of three different types of efficient modeling approaches for the prediction of the dimensions of pockets milled by AWJ technology. The models are developed and evaluated based on experimental results of AWJ pocket milling of a titanium workpiece by an eco-friendly walnut shell abrasive. The results indicate that a semi-empirical approach performs better than a two-step hybrid analytical/semi-empirical method regarding the selected cases, but both methods show promising results regarding the realistic representation of the pocket shape, which can be further improved by a probabilistic approach.

Modeling equilibrium and non-equilibrium thermophysical properties of liquid lubricants using semi-empirical approaches and neural network

Abstract This study explored the capability of semi-empirical and neural network approaches for correlating and predicting some equilibrium and non-equilibrium thermophysical properties of liquid lubricants. The equilibrium properties, including the densities and several thermodynamic coefficients for 12 liquid lubricants, were correlated and predicted through a perturbed hard-chain equation of state (PHC EoS) by an attractive term of Yukawa tail. The molecular parameters of PHC EoS were obtained by correlating them with 935 data points for the densities and isothermal compressibilities of studied systems in the 278–353 K range and pressure up to 70 MPa with the average absolute relative deviations (AARDs) of 0.36 % and 5.25 %, respectively. Then, that EoS was employed to predict the densities of other literature sources (with an AARD of 0.81 %) along with several thermodynamic coefficients, including isobaric expansivities (with an AARD of 12.92 %), thermal pressure coefficients (with the AARD of 12.93 %), and internal pressure (with the AARD of 13.67 %), for which the reference values were obtained from Tait-type equations and available in literature. Apart from the equilibrium mentioned above properties, the PHC EoS was combined with a rough hard-sphere-chain (RHSC) model to correlate and predict the 548 data points for the viscosities of 7 selected liquefied lubricants in 283–353 K range and pressures up to 100 MPa with the AARD of 11.85 %. The accuracy of the results from the RHSC-based model has also been compared with an empirical PηT equation of Tammann-Tait type and an artificial neural network (ANN), both of which were developed in this work. The ANN of one hidden layer and 13 neurons was trained using the back-propagation algorithm. The results acquired from this approach were very promising and demonstrated the potential of the ANN approach for predicting the viscosity of lubricants, reaching an AARD of 0.81 % for the entire dataset.

Tailoring the Effect of NiCo‐Metal‐Organic Frameworks by Doping Manganese as a Highly Active Redox Electrode for Electrochemical Energy Storage Technologies: Supercapattery Devices

Metal–organic frameworks with complex geometry have garnered significant attention in technological fields due to their diverse properties. Multimetallic metal‐organic frameworks (MOFs) have potential to demonstrate superior conductivity and efficiency compared to pristines, but, they have not been explored to research on large scale. Herein, significant effect of binary (NiCo‐) and ternary (NiCoMn‐) MOFs is explored with a novel approach using an organic ligand hexamethylene tetra‐amine (HMT). The as‐synthesized MOFs with their distinctive structures are thoroughly examined to analyze their structural, morphological, and electrochemical properties. In a standard half‐cell setup, the electrochemical behaviors of both samples are investigated, as both electrodes exhibit specific capacities of 349 and 905 C g−1, at 3 mV s−1, individually. Due to promising electrochemical responses of NiCoMnMOF, a supercapattery device is constructed as NiCoMnMOF//AC. This hybridization leads to the remarkable specific energy (78 Wh kg−1) and specific power (3500 W kg−1) with stability rate of ≈96% and Coulombic efficiency of 99% after undergoing 5000 galvanostatic charge–discharge cycles, making it highly suitable for modern applications. To gain better insight of supercapattery device, a semiempirical approach is employed, using Dunn's modulation. Thus, the synergy of NiCoMnMOF//AC in the single hybrid device exhibiting battery‐like characteristics holds tremendous potential for driving innovations in energy storage technology.

AM Bench 2022 Macroscale Tensile Challenge at Different Orientations (CHAL-AMB2022-04-MaTTO) and Summary of Predictions

The additive manufacturing benchmarking challenge described in this work was aimed at the prediction of average stress–strain properties for tensile specimens that were excised from blocks of non-heat-treated IN625 manufactured by laser powder bed fusion. Two different laser scan strategies were considered: an X-only raster and an XY raster, which involved a 90∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^\\circ $$\\end{document} rotation in the scan direction between subsequent layers. To measure anisotropy, multiple tensile orientations with respect to the build direction were investigated (e.g., parallel, perpendicular, and intervals in between). Benchmark participants were provided grain structure information via electron backscatter diffraction measurements, as well as the stress–strain response for tensile specimens manufactured parallel to the build direction and produced by the XY scan strategy. Then, participants were asked to predict tensile properties, like the ultimate tensile strength, for the remaining specimens and orientations. Interestingly, the measured mechanical properties did not vary linearly as a function of tensile orientation. Moreover, specimens manufactured with the XY scan strategy exhibited greater yield strength than those corresponding to the X-only scan strategy, regardless of orientation. The benchmark data have been made publicly available for anyone that is interested [1]. For the modeling aspect of the challenge, five teams participated in this benchmark. While most of the models incorporated a crystal plasticity framework, one team chose to use a more semiempirical approach and to great success. However, no team excelled at all the predictions, and all teams were seemingly challenged with the predictions associated with the X-only scan strategy.

Molecular Gas-Phase Conformational Ensembles.

Accurately determining the global minima of a molecular structure is important in diverse scientific fields, including drug design, materials science, and chemical synthesis. Conformational search engines serve as valuable tools for exploring the extensive conformational space of molecules and for identifying energetically favorable conformations. In this study, we present a comparison of Auto3D, CREST, Balloon, and ETKDG (from RDKit), which are freely available conformational search engines, to evaluate their effectiveness in locating global minima. These engines employ distinct methodologies, including machine learning (ML) potential-based, semiempirical, and force field-based approaches. To validate these methods, we propose the use of collisional cross-section (CCS) values obtained from ion mobility-mass spectrometry studies. We hypothesize that experimental gas-phase CCS values can provide experimental evidence that we likely have the global minimum for a given molecule. To facilitate this effort, we used our gas-phase conformation library (GPCL) which currently consists of the full ensembles of 20 small molecules and can be used by the community to validate any conformational search engine. Further members of the GPCL can be readily created for any molecule of interest using our standard workflow used to compute CCS values, expanding the ability of the GPCL in validation exercises. These innovative validation techniques enhance our understanding of the conformational landscape and provide valuable insights into the performance of conformational generation engines. Our findings shed light on the strengths and limitations of each search engine, enabling informed decisions for their utilization in various scientific fields, where accurate molecular structure determination is crucial for understanding biological activity and designing targeted interventions. By facilitating the identification of reliable conformations, this study significantly contributes to enhancing the efficiency and accuracy of molecular structure determination, with particular focus on metabolite structure elucidation. The findings of this research also provide valuable insights for developing effective workflows for predicting the structures of unknown compounds with high precision.

Innovative compressive strength prediction for recycled aggregate/concrete using K-nearest neighbors and meta-heuristic optimization approaches

This paper presents a groundbreaking method for predicting the compressive strength (Fc) of recycled aggregate concrete (RAC) through the application of K-nearest neighbors (KNN) analysis. The task of designing mixture proportions to achieve the desired Fc can be remarkably intricate, owing to the intricate interplay among the components involved. Machine learning (ML) algorithms have exhibited considerable promise in tackling this complexity effectively. In pursuit of enhanced prediction accuracy, this research introduces a semi-empirical approach that seamlessly integrates strategies, including optimization techniques. This study incorporates two meta-heuristic methods, the Fire Hawk optimizer (FHO) and Runge–Kutta optimization (RUK) to enhance model accuracy. The research results reveal three separate models: KNFH, KNRK, and a single KNN model, each providing valuable insights for precise Fc prediction. Remarkably, the KNFH model stands out as a top performer, boasting an impressive R2 value of 0.994 and a meager RMSE value of 1.122. These findings not only validate the accuracy and reliability of the KNFH model but also highlight its effectiveness in predicting Fc outcomes. This approach holds great promise for precise Fc forecasting in the construction industry. Integrating meta-heuristic algorithms significantly improves model accuracy, leading to more reliable forecasts with profound implications for construction projects and their outcomes. This research marks a significant advancement in predicting Fc using ML, offering valuable tools for engineers and builders.

Semiempirical molecular-orbital calculations of dissociation energies of small molecules containing light elements

A semiempirical molecular-orbital approach has been developed to calculate the dissociation energies of 57 second-row neutral and charged diatomic molecules. One-electron energies and wave functions were used. The 2s and 2p electrons have separate energies and separate values of Z 2S and Z 2P . Calculations were made with a CNDO/INDO FORTRAN programme modified to a level of approximation that is more similar to extended Hückel calculations than to either CNDO or INDO. The correlation between calculated and experimental dissociation energies was favourable with a relative standard deviation of 15%. Additionally, dipole moments for the molecules were calculated concurrently with the same parameters. The correlation between calculated and experimental dipole moments was favourable for seventeen molecules with a relative standard deviation of 19%. The correlation between calculated and experimental ionisation potentials for fourteen molecules was also favourable with a relative standard deviation of 11%. Also, a repulsion work function was formulated and used in this work between pairs of atoms. For 90% of the molecules studied, the maximum calculated dissociation energy occurred within ±0.1 Å of the experimental bond distance. The semiempirical methods used in the present work could be used for future studies of larger molecules, including organic molecules.