Energy Difference Research Articles

Association Between Ultra-Processed Food Consumption and Cognitive Performance Among Adolescent Students From Underdeveloped Cities in Brazil: A Cross-Sectional Study

ObjectivesThe association between ultra-processed foods (UPF) consumption and cognitive performance needs to be better characterized in adolescents, especially in low-income settings, where the cost of human capital is high. This study investigated the association between cognitive performance and UPF in adolescents from the countryside of the Brazilian Northeast.MethodsAdolescents (15–18 years old) from three public high schools were included. Food intake was assessed using three 24-hour dietary recalls. The classification of foods as UPF was determined according to the Nova classification. Cognitive performance was evaluated using the Non-Verbal General Intelligence Test.Results116 adolescents were included, of which 50 (43.1%) showed low cognitive performance. The average energy intake was 1973.5 kcal, with 24.2% coming from UPF. Participants with low cognitive performance consumed 26.5% (95% CI: [22.2; 30.7]%) of daily energy intake from UPF compared to 22.5% ([18.8; 26.2]%) of those with medium-high cognitive performance (P = 0.17), without differences in energy and macronutrient intake.ConclusionDespite similar UPF consumption compared to the Brazilian average, no association was found between UPF consumption and cognitive performance in this low-income adolescent sample.

Lowering of the singlet-triplet energy gap via intramolecular exciton-exciton coupling

Organic dyes typically have electronically excited states of both singlet and triplet multiplicity. Controlling the energy difference between these states is a key factor for making efficient organic light emitting diodes and triplet sensitizers, which fulfill essential functions in chemistry, physics, and medicine. Here, we propose a strategy to shift the singlet excited state of a known sensitizer to lower energies without shifting the energy of the triplet state, thus without compromising the ability of the sensitizer to do work. We covalently connect two to four sensitizers in such a way that their transition dipole moments are aligned in a head-to-tail fashion, but, through steric encumbrance, the delocalization is minimized between each moiety. Exciton coupling between the singlet excited states considerably lowers the first excited singlet state energy. However, the energy of the lowest triplet excited state is unperturbed because the exciton coupling strength depends on the magnitude of the transition dipole moments, which for triplets are very small. We expect that the presented strategy of designed intramolecular exciton coupling will be a useful concept in the design of both photosensitizers and emitters for organic light emitting diodes as both benefits from a small singlet-triplet energy gap.

Phase transition in the shape memory alloy NiTi described by the SCAN meta-GGA functional.

Climbing the ladder of density functional approximations has long been proposed to systematically improve the accuracy of first-principles calculations employing the density functional theory (DFT); however, up until now, the Perdew-Burke-Ernzerhof (PBE) functional at the second rung of the ladder, has dominated. Here, we present a study of the martensitic phase transition in NiTi based on abinitio molecular dynamics simulations and thermodynamic integration using the third-rung approximation of the strongly constrained and appropriately normalized (SCAN) meta-generalized gradient approximation (GGA). Although the predicted equilibrium lattice constants and formation enthalpy agree well with experimental data, the martensitic transition temperature (MTT) is overestimated by 94% (or 324K too high), compared with only 22% (77K) overestimation by PBE. The latent heat (q) is severely overestimated by SCAN as well. This deteriorated performance originates from the enlarged energy difference (ΔE) between the austenite and martensite phases, compared with the PBE result. Furthermore, a large variation (over 50meV/atom) in ΔE using different meta-GGAs indicates large variations in computed MTTs (∼400-500 K) and q, i.e., the predicted thermodynamic properties depend sensitively on the choice of meta-GGA. This would pose a serious problem when upgrading DFT calculations to the third rung. One possible solution is to add NiTi as a normsystem so that the revised SCAN meta-GGA could reproduce the PBE results of the relevant energy difference.

Automating Trace Detection and Chemical Purity Analysis of Carbon Nanotube Mixtures by Non-Negative Matrix Factorization of Spatial Raman Scattering Hyperspectra

Carbon nanotubes come in different species having different properties. So, it is useful to develop automated ways to quantify species purities and trace impurity content. Spatially scanned Raman spectra make hyperspectral data sets that can be used to discriminate between species and determine purities, and their analysis can be automated. A promising analytical machine learning approach is non-negative matrix factorization, which is a multivariate algorithm well adapted to hyperspectral Raman scattering data sets, and, significantly, available in open source. We prepare samples from different concentrations of pure nanotubes and acquire spatially scanned Raman scattering hyperspectra. We compare the known concentrations of the source dispersions to those determined from hyperspectra acquired from deposited materials on substrates. Here, we demonstrate and deal with several metrological issues: We show that the stability of the focusing conditions is critical. We show that if there are strong peaks that are not significant, normalization is helpful. We show that this approach compares favorably to the “best case” situation where the spectra are factored into a priori known library spectra. Scans of around 100 data points provided good bounds on concentrations down to about the parts per thousand level. Using more than one laser wavelength, so that different species are brought into resonance with each laser should enable higher relative purity measurements. However, there is an important consideration if the difference in laser energies is less than or comparable to the phonon energy. Overall, this approach is promising for the determination of chemical purity of carbon nanotubes and could be generalized to other chemical mixtures.

Determination of reduced density matrices in the doubly occupied configuration interaction space: A Hellmann-Feynman theorem approach.

In this work, the Hellmann-Feynman theorem is extended within the doubly occupied configuration interaction space to enable practical calculations of reduced density matrices and expected values. This approach is straightforward, employing finite energy differences, yet remains reliable and accurate even with approximate energies from successive approximation methods. The method's validity is rigorously tested against the Richardson-Gaudin-Kitaev and reduced Bardeen-Cooper-Schrieffer models using approximate excitation energies procured from the Hermitian operator method within the same space, effectively proving the approach's reliability with median error rates for reduced density matrix calculations around 0.1%. These results highlight the procedure's potential as a practical tool for computing reduced density matrices and expected values, particularly valuable as an ad hoc method in scenarios where only system energies are easily available.

Active Learning of Boltzmann Samplers and Potential Energies with Quantum Mechanical Accuracy.

Extracting consistent statistics between relevant free energy minima of a molecular system is essential for physics, chemistry, and biology. Molecular dynamics (MD) simulations can aid in this task but are computationally expensive, especially for systems that require quantum accuracy. To overcome this challenge, we developed an approach combining enhanced sampling with deep generative models and active learning of a machine learning potential (MLP). We introduce an adaptive Markov chain Monte Carlo framework that enables the training of one normalizing flow (NF) and one MLP per state, achieving rapid convergence toward the Boltzmann distribution. Leveraging the trained NF and MLP models, we compute thermodynamic observables such as free energy differences and optical spectra. We apply this method to study the isomerization of an ultrasmall silver nanocluster belonging to a set of systems with diverse applications in the fields of medicine and catalysis.

Study on Microstructure and Texture of Fe-3%Si Ultra-Thin Ribbons Prepared by Planar Flow Casting

In this paper, Fe-3%Si ultra-thin ribbons prepared by the planar flow casting (PFC) technique were subjected to temper rolling and annealing treatments. The microstructure and texture evolution during this process were examined through experimental measurements coupled with crystal plasticity finite element (CPFE) simulation to assess the feasibility of preparing ultra-thin non-oriented silicon steel using PFC ribbons. The results indicate that the PFC ribbons exhibit a significant columnar crystal structure, and {001}-oriented grains comprise over 30%. After being annealed, the grains with different orientations grew uniformly, the texture components were basically unchanged, and the {001} texture was well preserved. When annealing was carried out after temper rolling with a reduction rate of 7%, uneven grain growth was observed, and the growth tendency of the {001} grains, especially, surpassed that of the {111} grains, with an elevated temperature which peaked at 950 °C, where the proportion of {001} grains was maximal. When being annealed after temper rolling to 15%, grains of other orientations showed significant growth at each temperature, while the {001} grains did not show an obvious growth advantage. Utilizing the CPFE, the deformation-stored energy distribution of each characteristic-oriented grain was simulated, and it was shown that compared to the 15% rolling reduction rate, the deformation-stored energy accumulation of {001}-oriented grains after being rolled to 7% reduction was significantly lower than that of {111}-oriented grains. It suggests that the larger stored energy difference makes {001} grains show a stronger growth advantage based on the SIBM mechanism during annealing, after being rolled with a reduction rate of 7%. Overall, for the synergistic optimization of microstructure and texture, rolling with a 7% reduction rate followed by annealing at 950 °C in a hydrogen atmosphere is most advantageous.

Open String Renormalization Group Flow as a Field Theory

AbstractThis article shows that the integral flow‐lines of the RG‐flow of open string theory can be interpreted as the solitons of a Hořova–Lifshitz sigma‐model of open membranes. The authors argue that the effective background description of this model implies the g‐theorem of open string theory. Its close connection to boundary string field theory is described. Additionally, the study endows the Hilbert space of the open membrane with a graded non‐commutative, associative, cyclic algebra and construct an open membrane field theory, whose action measures the energy difference between different backgrounds in open string field theory. The authors use an identity‐based membrane field to proof Sen's conjecture. Finally, the ideas are applied to the topological string and it is shown that the membrane action is quantized in equivariant K‐theory of the moduli space of framed instantons.

Proton-Coupled Electron Transfer in Photoelectrochemical Alcohol Oxidation Enhanced by Nickel-based Cocatalysts.

Using biomass oxidation reactions instead of water oxidation reactions is optimal for accomplishing biomass conversion and effective hydrogen generation. Here, we report that Fe2O3 photoanodes with a NiOOH cocatalyst exhibit excellent performance for photoelectrochemical oxidation of 5-hydroxymethylfurfural (HMF) to 2,5-furandicarboxylic acid (FDCA). The conversion efficiency for HMF reaches 98.5%, while the selectivity for FDCA is 94.2%. We revealed that HMF is oxidized through a spontaneous proton-coupled electron transfer (PCET) process with the high-valent phase of the Ni-based catalyst. The dangling oxygen and bridging oxygen of the high-valent phase species serve as proton-accepting sites. Furthermore, we pointed out that the deprotonated bond dissociation free energy difference between the catalysts and alcohols is the thermodynamic trigger for the PCET process. This study provides a reasonable explanation for the alcohol oxidation reaction, which is beneficial for designing biomass conversion systems.

Data-Efficient Active Learning for Thermodynamic Integration: Acidity Constants of BiVO4 in Water.

The protonation state of molecules and surfaces is pivotal in various disciplines, including (electro-)catalysis, geochemistry, biochemistry, and pharmaceutics. Accurately and efficiently determining acidity constants is critical yet challenging, particularly when explicitly considering the electronic structure, thermal fluctuations, anharmonic vibrations, and solvation effects. In this research, we employ thermodynamic integration accelerated by com- mittee Neural Network potentials, training a single machine learning model that accurately describes the relevant protonated, deprotonated, and intermediate states. We investigate two deprotonation reactions at the BiVO4 (010)-water interface, a promising candidate for efficient photocatalytic water splitting. Our results illustrate the convergence of the required ensemble averages over simulation time and of the final acidity constant as a function of the Kirkwood coupling parameter. We demonstrate that simulation times on the order of nanoseconds are required for statistical convergence. This time scale is currently unachievable with explicit ab-initio molecular dynamics simulations at the hybrid DFT level of theory. In contrast, our machine learning workflow only requires a few hundred DFT single point calculations for training and testing. Exploiting the extended time scales accessible, we furthermore asses the effect of commonly applied bias potentials. Thus, our study significantly advances calculating free energy differences with ab-initio accuracy.

Chemical Pressure-Driven Band Convergence and Discordant Atoms Intensify Phonon Scattering Leading to High Thermoelectric Performance in SnTe.

SnTe is an intriguing alternative to PbTe for midtemperature thermoelectric applications. Despite steady progress, its performance is lagging, in part because of the large energy difference(ΔEL-Σ) between the light (L-band) and heavy (Σ-band) valence bands and higher lattice thermal conductivity (κL). Previous studies have shown that applying pressure can enhance the Seebeck coefficient (S) and power factor (PF) of SnTe. Inspired by this study, we showcase how the high-pressure effect can be emulated under ambient pressure by substituting Sn with atoms possessing smaller atomic radii. Specifically, Sb- and Ge-doping combined with CdTe- or CdS-alloying induce lattice shrinkage, also referred to as "chemical pressure", raising the energy of the Σ-band. Additionally, these substituted atoms lower the contribution of Sn 5s-Te 5p antibonding states to the L-band, thereby reducing its energy and dispersion. These combined effects decrease ΔEL-Σ from 0.36 to 0.09 eV, leading to the enhanced S and average PFavg. Notably, the PFavg, ranging from 323 to 873 K, increases from 8.1 μW cm-1 K-2 for pristine SnTe to 21.6 μW cm-1 K-2 for Sn0.79Ge0.15Sb0.06Te-5% CdTe. Furthermore, the intensified phonon scattering resulting from discordant nature of Ge and Cd atoms, creating point defects soften phonon modes, and the presence of Ge-rich nanoprecipitates lead to a substantial 62% reduction in κL at 873 K. This strong valence band convergence and enhanced phonon scattering collectively contribute to a high peak ZT of 1.5 (873 K) and high average ZTavg = 0.81 over the temperature range of 323-873 K.

Middle-aged dogs with low and high Aβ CSF concentrations show differences in energy and stress related metabolic profiles in CSF.

BackgroundAmyloid beta (Aβ) accumulation in the brain is one of the earliest findings in Alzheimer’s disease (AD). The dog is a natural animal model for amyloid processing and early brain amyloid pathology. The goal of this study is to examine which differences in metabolomic profiles in cerebrospinal fluid (CSF) could be detected in dogs with a difference in CSF Aβ concentrations before amyloid accumulation occurs. MethodMetabolic profiling was performed on CSF from 4 to 8 year old dogs with different CSF Aβ concentrations. ResultsMetabolomic profiling of CSF showed differences in brain energy metabolism. More specifically, increases in N-acetylation of amino acids and amino sugars, creatine and pentose metabolism, and a decrease in tricarboxylic acid (TCA) cycle were seen in dogs with a high CSF Aβ concentration. In addition, signs of elevated oxidative stress, higher methionine, lipid and nucleotide metabolism and increased levels of cysteine, myo-inositol and trimethylamine N-oxide were noted in these animals. ConclusionsDifferences in energy metabolism and stress mediated metabolic changes are seen in the brain of dogs with different CSF Aβ concentrations, before any amyloid deposition occurs. Similar metabolic changes, as in the high Aβ dogs, have been described in AD in humans and/or transgenic AD mice, some of them in very early phases. General significanceThe differences observed in metabolomic profiles could help in identifying potential biomarkers for an increased risk of developing amyloid pathology in the brain and open the door to the evaluation of preventive treatments for amyloid pathology in humans.

Determination of elastic modulus and anisotropy for rocks using digital drilling method

The main purpose of this study is to provide a digital drilling-based method for determining rock elastic modulus and evaluating the anisotropy. By considering a concept of crushed zone, the drilling response was characterized as the function of specific energy and drilling strength. On this basis, an equivalent elastic modulus was proposed to evaluate the rock elastic modulus. Moreover, an anisotropy index was proposed according to the difference in specific energy under different borehole directions. Digital drilling experiments were conducted for three lithologies at drilling directions namely 0, 60, and 120° (Based on vertical orientation), revealing the anisotropic effect exhibited in the rock. To validate the measurement of rock elastic modulus and its anisotropy, the compressive tests under same conditions were carried out. The results suggested that the proposed indicator provided an accurate evaluation for the rock's elastic modulus, with an error margin of less than 10%. Drilling-based anisotropy is identified in the drilling response, especially the critical state between cutting and friction. The statical indicator CoV (coefficient of variation) was utilized to characterize the anisotropy along the borehole. This research's innovation lies in providing an approach to evaluating rock anisotropy and determining its elastic modulus by considering the drilling response.

Applicability of Bernoulli's theorem to upstream and downstream of the bow shock

In this research, the validity of Bernoulli's theorem for the solar wind crossing the Earth's bow shock has been investigated based on space explorations. Using solar wind plasma and interplanetary magnetic field data from the OMNI database, along with magnetosheath plasma and magnetic field data measured by the magnetospheric multiscale satellite in 2016 and 2018, 95 events were selected to study the Bernoulli invariant from the upstream to the downstream of Earth's bow shock (from the unshocked solar wind region to the shocked magnetosheath region). Statistical investigations show that the differences in the characteristic energy of plasmas between the solar wind region and the magnetosheath region are less than 5%, regardless of the upstream solar wind speed and the satellite's location in the magnetosheath. These results quantitatively support the generalization of Bernoulli's equation to the solar wind flowing through the bow shock, including the contribution of magnetic field energy.

Inhibition of LPS-induced inflammatory response in RAW264.7 cells by natural Chlorogenic acid isomers involved with AKR1B1 inhibition

Inflammation is the physiological response of the immune system to injury or infection, typically manifested by local tissue congestion, swelling, heat, and pain. Prolonged or excessive inflammation can lead to tissue damage and the development of many diseases. The anti-inflammatory effects of natural ingredients have been extensively researched and confirmed. This study investigated the effects of Chlorogenic acid (CGA) isomers —— 3-Caffeolyquninic acid (3-CQA), 4-Caffeolyquninic acid (4-CQA), and 5-Caffeolyquninic acid (5-CQA) —— on the inflammatory response and oxidative stress reaction induced by LPS in RAW264.7 cells. Overall, 3-CQA exhibited the most significant reduction in levels of TNF-α, IL-6, NO, and ROS. 4-CQA showed superior inhibition of TNF-α compared to 5-CQA (p < 0.05), while no significant difference in other parameters. We further used DARTS and CETSA to demonstrate that CGA isomers have stable affinity with AKR1B1. As a positive control, the AKR1B1 antagonist epalrestat exhibited similar effects to the CGA isomers. 3-CQA having the smallest half-inhibitory concentration (IC50) for AKR1B1, while 4-CQA and 5-CQA have similar values. AutoDock simulations of the docking conformations revealed minimal differences in the average binding energies of the CGA isomers. The main differences were that VAL47 formed a hydrogen bond with 3-CQA, whereas GLN49 formed hydrogen bonds with 4-CQA and 5-CQA. Additionally, the number of hydrophobic bonds involving PHE122 and LEU300 varies. Our conclusion is that differences in non-covalent interactions result in the varying inhibitory abilities of CGA isomers on AKR1B1, which further affect the anti-inflammatory and antioxidant effects of CGA isomers.

Determining the carbon detection limit with magnetic sector dynamic Secondary Ion Mass Spectrometry (SIMS)

Dynamic SIMS is often used to evaluate the concentration of impurities in solids because of its high sensitivity, depth profiling capabilities, good depth resolution, and high throughput. Continuous ion beam sputtering with a high-density primary beam provides high sensitivity and reduces the background contribution from residual gases within the analytical chamber. Dynamic SIMS experiments performed with several magnetic sector instruments using various sputtering rate conditions demonstrated a slow decrease of the carbon detection limit with the increase of sputtering rate (SR), when it was varied by changing the sputtering beam density. The dependence data, within the scatter limits, can be fit by a power function of SR with the exponent of about -1/2. The observed detection-limit-dependence curve is common for magnetic sector SIMS instruments, owing to contamination and the design of the analytical chamber. The depth resolution of light-element SIMS analysis (except for oxygen) performed using Cs+ sputtering is limited and cannot be improved by reducing the impact energy of the sputtering beam. A segregation-type depth profile with a long trailing edge was observed in the B, C, and N depth profiles when Cs+ sputtering was employed under ultra-high vacuum conditions. This effect is plausibly associated with preferential sputtering owing to the difference in the surface binding energy, along with the large difference in the mass of the interacting atoms within the collision cascade during sputtering. The depth resolution improved with surface oxidation when Cs+ sputtering was combined with backfilling of the analytical chamber using O2. The analytical chamber backfilled with oxygen can be sufficiently replaced with a very low-impact energy-focused oxygen beam, enabling simultaneous oxygen and cesium co-sputtering SIMS analysis. The practical feasibility of using a co-sputtering with focused Cs+ and O2+ sputtering beams to achieve a high depth-resolution for carbon analysis under ultrahigh vacuum conditions was demonstrated.

Assessment of OH femtosecond thermally assisted fluorescence thermometry for hydrogen non-premixed flames

In this work, we further developed the planar temperature measurement method based on thermally assisted laser-induced fluorescence (TALF) with a single femtosecond (fs) laser and investigated the temperature distribution in the nitrogen-diluted oxygen-hydrogen cross-jet non-premixed flames at 1 kHz. Fs-TALF thermometry was firstly calibrated and assessed in a series of unconfined methane/air laminar flames at atmospheric pressure. In the calibration process, although the calibrated energy difference between OH v′ = 0 and 1 energy level was different from the theoretical value, the measured fluorescence ratio accurately reflected the temperature change from 1600 K to 2200 K in different methane flames. By comparing the calculated OH fluorescence spectrum, we believed that the main reason was the overlap of the OH (1-1) and (0-0) fluorescence bands. After the calibration, experiments were conducted with changing hydrogen flow velocity from 60 to 100 m/s and constant oxidizer flow velocity, where the dilution ratio, D varies from 0.46 to 0.7, and the equivalence ratio, ϕ, varies from 0.5 to 0.9. It is found, within the calibration range, fs-TALF thermometry can correctly measure the flame temperature. However, with ϕ further increases, the difference between the measured and theoretical temperatures gradually increases. At the same time, it was noted that the error in the measured temperature rises gradually as D decreases. In conclusion, fs-TALF thermometry can measure turbulent non-premixed hydrogen flames accurately in a range of temperature with acceptable temporal and partial resolution, while the errors within 200 K and spatial resolution is 170μm. The main error source should be the collisional effect due to the different local concentration of N2 and H2O. The results of this work will contribute to the further development the temperature measurement method hydrogen turbulent flames.

Captodative Versus Push‐Pull Aromatic Cyclopentadienyl Derivatives

ABSTRACTThe problem of whether and how the captodative aromatic systems with the donor and acceptor substituents at the same carbon atom of the C=C bond can be more stable than the π‐conjugated push‐pull counterparts with the two substituents in the vicinal position is analyzed for cyclopentadienyl derivatives possessing an aromatic cyclopentadienyl ring. The analysis of electronic, magnetic, and structural criteria of aromaticity showed that among typical organic donors, such as amines NR3, ethers R2O, guanidine (NH2)2C=NH, siloxane O(SiH3)2, silatrane, the captodative isomers [cyclopentadienylium]−C(X+) = CH2 can be more stable than their push‐pull isomeric counterparts [cyclopentadienylium]−—CH=CH—X+. The largest energy difference in favor of the former was found to be ca. 13 kcal/mol for X = NH=C(NH2)2.

Proton permeation and selective separation of hydrogen isotopes through fullerene nanocages (X12Y12): A DFT insights

To resolve the problem of mobile electrolytes for the permeation of hydrogen isotopes in fuel cells, we have studied herein heteronuclear fullerene nanocages (Al12N12, Al12P12, B12N12, and B12P12) for the separation and permeation of hydrogen isotopes. The separation and permeation of fullerene nanocages for hydrogen isotopes are studied at ωB97XD functional of DFT along with 6–31g (d,p) pople basis set. Zero-point energy (ZPE) is calculated for Protium (H+) and its heavier isotopes including deuterium (D+) and tritium (T+). The Arrhenius equation is employed to calculate the selectivity of protium to deuterium (H+/D+) and deuterium to tritium (H+/T+) isotopes. The selectivity is calculated by using the zero-point energy difference (ZPE) of protium (H+) to its heavier isotopes. The proposed fullerene nanocages estimated better selectivity for proton isotopes. The phosphide-based nanocages (Al12P12, B12P12) provide superior selectivity values of H+/D+ and H+/T+ compared to nitrogen-based nanocages (Al12N12, B12N12). This study provides insights into separation pathways for proton isotopes through nanocages for many industrial applications in the energy sector.

Mongrel synthesis of tin (IV) based 3-aminopyridine with hydrogen halides: Structural, optical, thermal analysis, DFT, Hirshfeld surface analysis and antibacterial investigations

The novel organic-inorganic hybrid materials (C5H7N4)2 [SnCl6] 2- (1) and (C5H7N4)2 [SnBr6]2-(2) have been synthesized and characterized by single-crystal X-ray diffraction at room temperature. Both compounds demonstrate the crystal system was triclinic with Pī space group. Hirshfeld surface analysis investigate the intermolecular interactions and crystal packing derived by single-crystal XRD data reveals the closer contacts associated with strong interactions. Infrared spectrum of both compounds shows the NH stretching band at 3396 cm−1 which indicates a protonated amine group. The product crystals optical characteristics were inspected using UV–visible and photoluminescence spectroscopy. The thermal characteristics were evaluated simultaneously using thermogravimetric (TG) and differential thermal analysis. Tin formal oxidation state was determined as +4 through bond valence sum calculations and CSM shows the ideal octahedral geometry of the anions. DFT calculations utilizing the B3LYP method with the LanL2DZ basis set provide optimized geometries and molecular electrostatic potential maps. We explored the energy difference between (HOMO) and (LUMO) orbitals to understand molecular electrical transport capabilities. The biological activity of the investigated compounds are examined against pathogens which indicates efficacy against bacterial strains.