Experimental Determination Research Articles

Computational Prediction of Multiple Antigen Epitopes.

Identifying antigen epitopes is essential in medical applications, such as immunodiagnostic reagent discovery, vaccine design, and drug development. Computational approaches can complement low-throughput, time-consuming, and costly experimental determination of epitopes. Currently available prediction methods, however, have moderate success predicting epitopes, which limits their applicability. Epitope prediction is further complicated by the fact that multiple epitopes may be located on the same antigen and complete experimental data is often unavailable. Here, we introduce the antigen epitope prediction program ISPIPab that combines information from two feature-based methods and a docking-based method. We demonstrate that ISPIPab outperforms each of its individual classifiers as well as other state-of-the-art methods, including those designed specifically for epitope prediction. By combining the prediction algorithm with hierarchical clustering, we show that we can effectively capture epitopes that align with available experimental data while also revealing additional novel targets for future experimental investigations. raji@yu.edu. Supplementary data are available at Bioinformatics online.

On-Surface Synthesis of 2D Polymers at the Liquid-Solid Interface

Unraveling the structure and dynamics of the formation of covalent and non-covalent organic-based 2D crystalline materials is key to controlling the quality of these materials, such as the defect density and their size. Understanding these characteristics is important for controlling their properties. This contribution highlights our efforts in using scanning probe microscopy, particularly scanning tunneling microscopy (STM), to visualize the structure and dynamics of substrate-supported metal-organic frameworks (sMOFs) and substrate-supported covalent organic frameworks (sCOFs) at the liquid-solid interface.We will focus on the growth of covalent organic frameworks, primarily based on boroxine chemistry, and reveal both qualitative and quantitative details of the nucleation-elongation processes in real-time and under ambient conditions. Sequential data analysis enables the observation of the amorphous-to-crystalline transition, the time-dependent evolution of nuclei, the existence of 'non-classical' crystallization pathways, and, importantly, the experimental determination of essential crystallization parameters with excellent accuracy, including critical nucleus size, nucleation rate, and growth rate. Other aspects that will be addressed include sCOFs and chirality, as well as the formation of multilayered sCOFs. Additionally, we demonstrate that in specific cases, the electric field between the STM tip and the substrate can induce, on demand, the polymerization or depolymerization process.

Comparison of Machine Learning Approaches for Prediction of the Equivalent Alkane Carbon Number for Microemulsions Based on Molecular Properties.

The chemical properties of oils are vital in the design of microemulsion systems. The hydrophilic-lipophilic difference equation used to predict microemulsions' phase behavior expresses the oils' physiochemical properties as the equivalent alkane carbon number (EACN). The experimental determination of EACN requires knowledge of the temperature dependence of the microemulsion system and the effects of different surfactant concentrations. Thus, the experimental determination is time-intensive and tedious, requiring days to months for proper separations. Furthermore, the experiments require high purity of chemicals because microemulsions are sensitive to impurities. Our work focuses on the quick and reliable predictions of the EACN with machine learning (ML) models. Due to the immaturity of ML chemical predictions, we compare three graph neural networks (GNNs) and a gradient-boosted tree algorithm, known as XGBoost. The GNNs use the molecular structures represented as simplified molecular-input line-entry system (SMILES) codes for the initial input, which allows us to assess whether geometry optimization is necessary for reliable results. The XGBoost model also begins with the SMILES representations of the molecules but uses molecular descriptors instead of geometry optimizations. The best model tested (crystal graph convolutional neural network with Merck molecular force field-94) has an error of 1.15 EACN units of the true EACN for unknown data with the errors skewed toward zero and an R2 score of 0.9.

Liquid and crystallized phases stability in the sub-system H3PO4[sbnd]H4P2O7: Experimental determination and modeling

The study of the system formed by ortho- and pyrophosphoric acid was resumed in order to understand the crystallization conditions of these two compounds and to highlight the existence of their possible polymorphism. To this end, the solid-liquid equilibria (SLE) of pyrophosphoric acid was studied in depth. Contradictions in literature data were resolved through systematic experimentation: solubility measurements, differential scanning calorimetry (DSC), and X-ray diffraction (XRD). Calorimetric measurements confirmed the existence of two crystalline forms of pyrophosphoric acid, and their stability domains were determined. Furthermore, thermodynamic modeling of the SLE has led to a consistent and refined representation of the observed phenomena. In particular, the transition temperature from low-temperature (form I) to high-temperature form (form II) of pyrophosphoric acid was determined at 298.4 K and the coordinates of the eutectic point common between H3PO4 and H4P2O7 (I) were precisely determined. Modeling also confirms the non-negligible quantity of triphosphoric acid in the liquid state throughout virtually the entire compositional range. Finally, X-ray powder diffraction data were used to determine the cell parameters and space group of pyrophosphoric acid using EXPO 2014 software.

Synergetic recovery of Ti and Fe from Ti-bearing blast furnace slag and red mud by diamond wire saw silicon waste

Diamond wire saw silicon waste (DWSSW), Ti-bearing blast furnace slag (TBFFS), and red mud (RM) are three types of industrial wastes that contain abundant Si, Ti, and Fe. Their recovery is vital for reducing the waste of resources, environmental protection, and sustainable industrial development. In this study, the environmentally friendly process of synergetic recovery of Ti and Fe from TBFS and RM to prepare Si-Ti-Fe alloy by using DWSSW as reducing agent was proposed. The thermodynamic and experimental determinations of the reason behind Si extracting Ti from TBFS and RM is that it decreased the activity of SiO2. The optimal conditions for recovery of Ti and Fe were that the mass ratio of RM to TBFS was 1, and the mass ratio of DWSSW to (RM+TBFS) was 1.25. The highest recovery rates of Ti and Fe are 88.67 % and 93.33 %, respectively. Further, the Si-Ti-Fe alloy was separated and purified by electromagnetic directional solidification to obtain Si and Si-Fe-Ti alloys. Through life cycle assessment (LCA), it is concluded that electricity during the recycling process has the most significant impact on the environment. This process is a green and efficient method for the recovery of DWSSW, TBFS, and RM.

Calculation of the formation enthalpies of charge-transfer complexes with iodine from the binding constants at 298.15 K

There is a continuing interest in charge-transfer complex chemistry, particularly in their formation thermodynamics. Nowadays, the binding constants and Gibbs energies of formation are available for many complexes. However, the experimental determination of the complexation enthalpies is a more difficult procedure, as it usually requires the establishment of the temperature dependence of the binding constant. Our recent studies of the compensation relationship in the thermodynamics of solvation in hydrogen-bonded systems opened the way to the calculation of the enthalpies of specific (i.e., localized donor-acceptor) interactions (Δint(sp)H) directly from the Gibbs energies at 298.15 K (Δint(sp)G). The calculation procedure is based on the following equation: Δint(sp)G/(kJ·mol−1) = 0.66·Δint(sp)H/(kJ·mol−1) + 2.5n (n corresponds to the coordination number of a solute).In the present work, we checked whether the above relation can be applied to the complex formation between halogens (primarily iodine) and organic electron donors. For 152 complexes with aromatics, nitrogen, oxygen, sulfur, selenium, and phosphor-containing compounds, the formation enthalpies were estimated using the data on equilibrium constants at 298.15 K. In most cases, agreement with the experimental binding enthalpies within the expected measurement error was observed (RMS = 1.4 kJ·mol−1, AD = –0.4 kJ·mol−1).

Experimental determination and theoretical modeling of isobaric vapor–liquid equilibria, liquid mass density, surface tension and dynamic viscosity for the methyl butyrate and tert-butanol binary mixture

The purpose of this work is to provide consistent experimental measurements on the vapor–liquid equilibria (VLE) at the isobaric conditions of 50.00, 75.00 and 94.00kPa, as well as experimental determinations of the liquid mass densities, the surface tensions and the dynamic viscosities at 298.15K and 101.3kPa of the methyl butyrate + tert-butanol binary system within the whole composition range. For these goals, a Gillespie-type cell is used to carry out the VLE measurements, whereas an oscillating densimeter, a maximum differential bubble pressure tensiometer, and a rotating viscometer are used to quantify the remaining physical properties. The thermodynamical quality of the reported VLE data is validated by Fredenslund’s consistency test. Based on the VLE results, this zeotropic binary mixture exhibits a positive deviation from Raoult’s law. Furthermore, the validated data are correlated with three activity coefficient models (i.e., Wilson, NRTL, and UNIQUAC), with the Wilson equation being the most accurate one. Excess volumes, surface tensions, and liquid viscosities data are correlated by Redlich–Kister-type expansions, and the Grunberg–Nissan equation is also applied to viscosity modeling. The calculated excess volumes display a positive deviation from the ideal solution model. Regarding surface tensions, this system showcases both negative and positive deviation, althouhg slight, from the linear behavior while exhibiting the linear trend at a molar fraction of methyl butyrate of 0.758. In addition, this bimodal deviation is adequately predicted by the Chunxi model with the Wilson parameters determined from the VLE data. Finally, the dynamic viscosities data obey a strictly monotonic mole dependence predicted by Eyring’s theory, although without high accuracy. These results also reflect some interesting phenomena related to competitive association effects, which are discussed.

CORTEX experiments, Part III: Experimental determination of the zero power transfer function of AKR-2 with reliable uncertainties

The transfer function is an important characteristic quantity of a nuclear reactor, since it contains the kinetic parameters. It expresses the response of a nuclear reactor to a disturbance of a certain frequency. If it is determined experimentally, it can be used to draw conclusions about the kinetic parameters. This article presents results of measurements of the zero power transfer function of the AKR-2 reactor at TU Dresden together with new data analysis methods. These measurements are compared to the theoretical zero power transfer function with kinetic parameters obtained via Monte Carlo simulations with MCNP and Serpent. To this end, advanced data analysis techniques based on a bootstrapping algorithms are employed. These techniques suppress the signal outside multiples of the fundamental frequency and additionally allow to obtain the full probability distribution of a peak in the frequency domain. This allowed for a reliable estimation of the mean value and uncertainty estimates of measured data of the zero power transfer function and the quantification of deviations between the experiments and the computations. It also made it possible to determine the phase of the zero power transfer function of AKR-2 for the first time. The experiments and computations are in agreement within the estimated uncertainties.

Experimental Determination of Si Self-Interstitial Emission During Oxide Precipitation in Czochralski Silicon

We used the method of Torigoe and Ono [J. Appl. Phys., 121, 215103 (2017)] to investigate the kinetics of β, the number of self-interstitials emitted per precipitated oxygen atom, during oxide precipitation in Czochralski silicon. For this purpose, we used pp- epitaxial wafers with a buried highly B-doped epitaxial layer which were annealed with and without thermal pre-treatments at 950 °C. From the results we conclude that in the initial phase of oxide precipitation without thermal pre-treatment β is very high before it drops to low values. With a thermal pre-treatment at 800 °C for 2 h, the initial value of β is somewhat lower before the drop also occurs. If a nucleation anneal is carried out before the thermal treatment at 950 °C the β values are low from the beginning. All of these results confirm our previously published theoretical predictions experimentally. This work also shows that the crystal pulling process can affect the initial β value because grown-in oxide precipitate nuclei can reduce their strain by vacancy absorption. Therefore, high vacancy supersaturation during crystal cooling while oxide precipitate nucleate would lead to somewhat lower initial β values.

CUGAS2 КРИСТАЛЛДАРЫ, АЛАРДЫН МҮНӨЗДӨМӨЛӨРҮ ЖАНА ИШТЕТИЛИШИ

In this article, the characteristics of CuGaS2 crystals grown are studied by the gas transport method and the two-temperature synthesis method. The described materials, primarily original ones, as well as the presented materials, allow us to conclude that a wide range of compounds represents an unexplored class of substances, especially in the field of experimental determination of their physic-chemical properties, methods of crystal growth and correlation of material characteristics with technological parameters of the production processes. The growing practical application of semiconductors stimulates research into new materials. In recent years, much attention has been paid to studying the physical properties of multicomponent systems, especially crystals of groups I-II-VI2, which are promising for many technical applications. For a number of crystals of group I-II-VI2, methods for selecting p- and n-type samples have been developed, on the basis of which heterojunctions have been created.

Experimental Determination of Mechanical Properties and Characterization of Selected Crop Residues

The management of huge waste generated from crop harvesting and processing has continued to create challenges and constitute an environmental nuisance. Inappropriate disposal and open-air burning of crop residues exacerbate environmental pollution, escalate bush burning and deforestation, and impact human health. Mixing, processing, and conversion of crop residues to form useful composites for various applications remain one of the economical, eco-friendly, and sustainable strategies for its management. The study constructed composites by mixing different ratios of unripe plantain peel (UPP) and coconut fibre (CCF) with an appropriate binder and hardener. The fabricated composites were subjected to mechanical, compositional, and morphological analyses. The outcomes of the tests show that the hardness, tensile strength, and impact strength only UPP is 97.8 RHN, 411 MPa, and 9 818 J/m2, respectively while the CCF/UPP composite is 98.5 RHN, 538 MPa, and 12 273 J/m2, respectively. The wear rate of UPP is 0.56 cm3/m while that of the CFF/UPP composite is as high as 0.73 cm3/m and increases with increased load. Silicon, oxygen, and aluminium are the major constituents of the composite samples as revealed by the compositional analysis. The tensile strength, hardness, impact, and wear rate of UPP can be boosted by the blending of CFF to form homogenous composites. The outcome of this study will deepen the literature and escalate research into the conversion and utilization of crop residues for diverse applications. The usage of innovative technologies and energy-efficient techniques should be adopted for the processing, modification, and conversion of crop residues.

Experimental Determination of Phase Equilibria in the La-Co-Zr System

Experimental Determination of Phase Equilibria in the La-Co-Zr System

Material Compatibility in 4D Printing: Identifying the Optimal Combination for Programmable Multi-Material Structures.

This study identifies the optimal combination of active and passive thermoplastic materials for producing multi-material programmable 3D structures. These structures can undergo shape changes with varying radii of curvature over time when exposed to hot water. The research focuses on examining the thermal, thermomechanical, and mechanical properties of active (PLA) and passive (PRO-PLA, ABS, and TPU) materials. It also includes the experimental determination of the radius of curvature of the programmed 3D structures. The pairing of active PLA with passive PRO-PLA was found to be the most effective for creating complex programmable 3D structures capable of two-sided transformation. This efficacy is attributed to the adequate apparent shear strength, significant differences in thermomechanical shrinkage between the two materials, identical printing parameters for both materials, and the lowest bending storage modulus of PRO-PLA among the passive materials within the activation temperature range. Multi-material 3D printing has also proven to be a suitable method for producing programmable 3D structures for practical applications such as phone stands, phone cases, door hangers, etc. It facilitates the programming of the active material and ensures the dimensional stability of the passive components of programmable 3D structures during thermal activation.

Extended characteristic polynomial estimating the electrochemical behaviour of some 4-(azulen-1-yl)-2,6-divinylpyridine derivatives

Six derivatives of 4-(azulen-1-yl)-2,6-divinylpyridine were the subject of experimental determination of oxidation and reduction potentials being reported elsewhere. In this paper, a computational study was employed in order to obtain a function of structure for these potentials. The geometry was optimized at three theory levels (MMFF94, B3LYP and M06), and the following analysis was conducted with the separately saved optimum geometry in each instance. Two families of molecular descriptors (FMPI and EChP) were used to derive structure-based descriptors. Simple linear regressions were extracted with the best of descriptors for each family and level of theory for both potentials. The study revealed that the MMFF94 optimum geometries best explained the selected electro­chemical properties. Furthermore, the EChP family of descriptors, much bigger than FMPI (about 64 times), was able to better explain the connection between the structure and the property. Once more, it has been shown that the eigenproblem has deep roots in structural chemistry.

Experimental determination of stray currents in parallel operated cells exemplified on alkaline water electrolysis

Avoiding undesired electric field-driven cell-to-cell ion conduction is a common issue in electrochemical bipolar stacks affecting many processes such as alkaline water electrolysis, chlor-alkali electrolysis, and redox flow batteries. In bipolar stack systems, they are known as ionic shunt or stray currents and flow alongside a cell stack through the electrolyte manifolds bypassing the bipolar plates. A similar phenomenon occurs if multiple single cells are operated in parallel with a common electrolyte supply, even though each individual cell is galvanically isolated. If the parallel cells are operated at different voltages, an electrochemical potential gradient is formed across the electrolyte tube manifold, causing stray currents to flow from one cell to the other. It is important to determine and reduce stray currents and its implications to ensure high system efficiency and maximum system lifetime. This work presents a new approach with which stray currents between parallel operated, galvanically isolated electrochemical cells has been determined for the first time. For this purpose, an ionic 4-point measurement was implemented into an experimental setup with two electrolyzer flow cells with a common electrolyte supply using reference electrodes. This approach was used to measure the potential differences ΔE between both electrolyzers exemplarily for alkaline water electrolysis. Combined with the measured ionic resistances of the electrolyte within the polymeric tube manifold, stray currents were accurately determined. The presented results show that the ionic 4-point measurement could be successfully implemented and validated to experimentally determine stray currents between parallelly operated cells. The method presented enabled stray current measurements with high precision in the mA range. Due to the high cell currents and high ionic tube resistance, the stray current is less than 1 % in the introduced electrochemical setup. The demonstrated systematic measurement approach can be easily transferred to other electrochemical systems or processes.

MPEK: a multitask deep learning framework based on pretrained language models for enzymatic reaction kinetic parameters prediction.

Enzymatic reaction kinetics are central in analyzing enzymatic reaction mechanisms and target-enzyme optimization, and thus in biomanufacturing and other industries. The enzyme turnover number (kcat) and Michaelis constant (Km), key kinetic parameters for measuring enzyme catalytic efficiency, are crucial for analyzing enzymatic reaction mechanisms and the directed evolution of target enzymes. Experimental determination of kcat and Km is costly in terms of time, labor, and cost. To consider the intrinsic connection between kcat and Km and further improve the prediction performance, we propose a universal pretrained multitask deep learning model, MPEK, to predict these parameters simultaneously while considering pH, temperature, and organismal information. Through testing on the same kcat and Km test datasets, MPEK demonstrated superior prediction performance over the previous models. Specifically, MPEK achieved the Pearson coefficient of 0.808 for predicting kcat, improving ca. 14.6% and 7.6% compared to the DLKcat and UniKP models, and it achieved the Pearson coefficient of 0.777 for predicting Km, improving ca. 34.9% and 53.3% compared to the Kroll_model and UniKP models. More importantly, MPEK was able to reveal enzyme promiscuity and was sensitive to slight changes in the mutant enzyme sequence. In addition, in three case studies, it was shown that MPEK has the potential for assisted enzyme mining and directed evolution. To facilitate in silico evaluation of enzyme catalytic efficiency, we have established a web server implementing this model, which can be accessed at http://mathtc.nscc-tj.cn/mpek.

GR-pKa: a message-passing neural network with retention mechanism for pKa prediction.

During the drug discovery and design process, the acid-base dissociation constant (pKa) of a molecule is critically emphasized due to its crucial role in influencing the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties and biological activity. However, the experimental determination of pKa values is often laborious and complex. Moreover, existing prediction methods exhibit limitations in both the quantity and quality of the training data, as well as in their capacity to handle the complex structural and physicochemical properties of compounds, consequently impeding accuracy and generalization. Therefore, developing a method that can quickly and accurately predict molecular pKa values will to some extent help the structural modification of molecules, and thus assist the development process of new drugs. In this study, we developed a cutting-edge pKa prediction model named GR-pKa (Graph Retention pKa), leveraging a message-passing neural network and employing a multi-fidelity learning strategy to accurately predict molecular pKa values. The GR-pKa model incorporates five quantum mechanical properties related to molecular thermodynamics and dynamics as key features to characterize molecules. Notably, we originally introduced the novel retention mechanism into the message-passing phase, which significantly improves the model's ability to capture and update molecular information. Our GR-pKa model outperforms several state-of-the-art models in predicting macro-pKa values, achieving impressive results with a low mean absolute error of 0.490 and root mean square error of 0.588, and a high R2 of 0.937 on the SAMPL7 dataset.

Experimental Vapor Pressure Determination for C2H4, C2H6, CH3OH, CH4, CO, CO2, H2O, and N2 Molecules for Astrophysical Relevant Temperatures. Implications for the Presence of Volatiles in Kuiper Belt Objects and Trans-Neptunian Objects

Vapor pressure is a relevant quantity that is necessary in order to improve the study of the atmosphere dynamics that take place within astrophysical scenarios. The aim of this study was to obtain the vapor pressure values of the following molecules: C2H4, C2H6, CH3OH, CH4, CO, CO2, H2O, and N2 through experimentation, as well as to determine their empirical relationship with the temperature, applying the results to the persistence of volatiles in trans-Neptunian objects (TNOs) and Kuiper Belt objects (KBOs). The experimental determination was performed by measuring the sublimation rate for each molecule at different temperatures. The Hertz–Knudsen equation was used to obtain the vapor pressures for the aforementioned molecules, taking the necessary considerations into account, and the sublimation rate was measured using a quartz crystal microbalance. In order to check the validity of the methods used, the results obtained for water ice were compared with those of previous studies from the literature. The values obtained for CO, N2, and CH4 are of particular interest in the study of the TNOs' and KBOs' atmosphere composition. The results of this study improve the understanding of the surface and atmospheric composition of objects in the cold scenarios of the solar system, in particular, in KBOs and TNOs.

Experimental and Statistical Determination of Convective Heat and Mass Transfer Coefficients for Eucalyptus nitens Sawn Wood Drying

This study aimed to develop a model using experimentally obtained convective heat and mass transfer coefficients to predict the effect of temperature, humidity, and drying rate on wood drying. Tangential wood samples of Eucalyptus nitens (H. Deane & Maiden) were used in the investigation. The experimental design consisted of two temperature levels (40 °C and 55 °C), two relative humidity levels (55% and 75%), and two air velocity settings (2 m·s−1 and 3 m·s−1). The experiments were conducted under a constant evaporation rate, spanning the maximum and critical moisture content in the wood. A statistical model using multivariate regression was created to predict the convective heat and mass transfer coefficients. The results indicated that the experimental data and empirical correlations exhibited an error margin of 37.77% and 37.86%, respectively. A significant positive correlation was found between the convective heat transfer coefficient and air velocity, temperature, and relative humidity, while the convective mass transfer coefficient showed a significant positive correlation only with air velocity and temperature. The model predicted the convective heat and mass transfer coefficients with high accuracy and statistical significance. Using the proposed method, we successfully obtained both convective coefficients, which enable accurate description of heat and mass flow during the convective drying of Eucalyptus nitens wood.

In silico and in vitro chemometrics, cell toxicity and permeability of naringenin 8-sulphonate and derivatives.

Sulphur containing natural compounds are among the most biologically relevant metabolites in vivo. Naringenin 8-sulphonate from Parinari excelsa Sabine was evaluated in a previous work, demonstrating ability to act as a natural anti-inflammatory. Although the interference of this molecule against different inflammatory mediators was described, there is no information regarding its potential toxicity and pharmacokinetics, which are essential for its capacity to reach its therapeutic targets. In fact, despite the existence of reports on naringenin ADMET properties, the influence of sulphation patterns on them remains unknown. This work aims to assess the in vitro pharmacokinetic and toxicological behavior of naringenin 8-sulphonate, as well as to understand the importance of the presence and position of the sulphur containing group for that. Naringenin 8-sulphonate physicochemical and ADMET properties were investigated using in silico tools and cell-based in vitro models. At the same time, naringenin and naringenin 4'-O-sulphate were investigated to evaluate the impact of the sulphonate group on the results. ADMETlab 2.0 in silico tool was used to predict the compounds' physicochemical descriptors. Pharmacokinetic properties were determined experimentally in vitro. While MRC-5 lung fibroblasts and HaCaT keratinocytes were used to evaluate the cytotoxicity of samples through MTT and LDH assays, Caco-2 human intestinal epithelial cells were used for the determination of genotoxicity, through alkaline comet assay, and as a permeability model to assess the ability of compounds to cross biological barriers. Experimental determinations showed that none of the compounds was cytotoxic. In terms of genotoxicity, naringenin 8-sulphonate and naringenin caused significant DNA fragmentation, whereas naringenin 4'-O-sulphate did not. When it comes to permeability, the two sulphur-containing compounds with a sulphur containing group were clearly less capable to cross the Caco-2 cell barrier than naringenin. In this study, we conclude that the sulphur containing group from naringenin 8-sulphonate is disadvantageous for the molecule in terms of ADMET properties, being particularly impactful in the permeability in intestinal barrier models. Thus, this work provides important insights regarding the role of flavonoids sulphation and sulphonation upon pharmacokinetics and toxicity.