Ternary Data Research Articles

Liquid-Solid Phase Diagrams for Lithium-Ion Battery Electrolytes in the Ternary Space and Beyond

Although practical lithium-ion battery electrolytes generally utilize two or more solvents in addition to the dissolved lithium salt, liquid-solid phase diagrams have, as far as the authors know, only been reported for binary single-solvent electrolytes. This precludes rational design of low freezing point liquid electrolyte mixtures. We herein present a thermodynamic framework to draw out ternary electrolyte liquidus surfaces using the comprising binary liquid-solid equilibria data and limited ternary data measured via calorimetry. LiPF6 in liquid carbonate mixtures are here studied. The introduced thermodynamic framework also allows evaluation of the thermodynamic factor, which appears in the context of transport parameter measurements.

Coupled phase diagram study and thermodynamic modeling of the Na2O-B2O3-ZnO system

The Phase diagram of ternary Na2O-B2O3-ZnO system was investigated for the first time using the classical equilibration/quenching method and differential thermal analysis followed by the phase analysis using the Electron Probe Micro-Analysis (EPMA) and X-Ray Diffraction (XRD). The isothermal phase diagrams of this ternary system at 900, 1000 and 1100 °C were determined, and the melting temperatures of two ternary compounds were investigated using Differential Thermal Analysis (DTA). The thermodynamic modeling of ternary Na2O-B2O3-ZnO system was carried out based on the present ternary phase diagram data and the optimized model parameters of binary systems. Thermodynamic models with optimized model parameters were used to predict the phase diagram of the ternary Na2O-B2O3-ZnO system.

Prediction of azeotropes position of refrigerant mixtures using the PHSC EoS

The work aims is predict the azeotropes' position in refrigerant mixtures using the Perturbed Hard Sphere Chain (PHSC) equation of state (EoS). The vapor-liquid equilibrium (VLE) of seventeen binary refrigerant mixtures has been correlated using two model-adjustable parameters. The results show that the proposed model can calculate the bubble and dew points of binary systems, accurately. The average ARD value of binary VLE calculations has been obtained 1.28 %. The azeotropes pressure and composition of binary mixtures have been predicted using the PHSC EoS with the average ARD values of 1.043 % and 2.23 %, respectively. The maximum relative deviation has been observed for R717+R125 systems with two azeotropes points; ARDx=12.2 %. The performance of the PHSC EoS has been evaluated by predicting the azeotropes position of ternary mixtures. The PHSC EoS results have been compared to three cubic-based EoSs containing the PR-vdW, PR-BM, and PR-MHV2-NRTL models. The results show that the PHSC EoS can predict the ternary VLE data and azeotropes position satisfactory. The relative absolute deviation does not exceed 1.7 % for the azeotropes pressure. The PHSC EoS can be used as a robust and efficient model for the prediction of the azeotropes' position in binary and ternary refrigerant mixtures. Prediction of azeotropic composition helps us to find promising refrigerant candidates in the pre-design stage of the desired apparatus.

Phase behavior and intermolecular interaction analysis of the ternary system of water + 2-methylpyridine + octanols from 303.2 K to 323.2 K

Wastewater from coking and pharmaceutical processes often contain a significant amount of 2-methylpyridine, which forms an azeotropic mixture with water. Effectively and cleanly separating 2-methylpyridine from wastewater while conserving energy and obtaining high purity products is an urgent challenge. In this study, ternary liquid-liquid equilibrium (LLE) data for the extraction of 2-methylpyridine from wastewater were measured using three distinct solvents at temperatures of 303.2 K, 313.2 K, and 323.2 K. The obtained ternary data were subsequently correlated with thermodynamic models (NRTL and UNIQUAC) to get relevant binary interaction parameters, bridging a gap in Aspen Plus simulations of 2-methylpyridine separation by octanol solvents. Furthermore, the GUI tool was used to validate these parameters and the results confirmed the reliability of the obtained parameters. It was observed that as the temperature increased, the extraction performance of all systems decreased, indicating that elevated temperature are not conducive to the extraction process. Among the various components, iso-octanol showed the highest separation factor indicating better extraction efficiency of 2-methylpyridine. Additionally, this study integrated quantum chemical calculations to reveal the interaction mechanisms between different solvents, 2-methylpyridine, and water at the microscopic level. The computational results indicated that iso-octanol exhibited the most robust binding affinity with 2-methylpyridine, suggesting that iso-octanol has more excellent extraction properties.

Single and multicomponent adsorption of amoxicillin, ciprofloxacin, and sulfamethoxazole on chitosan-carbon nanotubes hydrogel beads from aqueous solutions: Kinetics, isotherms, and thermodynamic parameters

The removal of antibiotics in water receiving bodies is an integral part in eradicating antimicrobial resistance which has become a major threat to public health. Solid-liquid adsorption has demonstrated to be an effective technique in the removal of antibiotics from aqueous environments. Herein, chitosan-carbon nanotube (chitosan-CNT) hydrogel beads were synthesised for the adsorption of amoxicillin (AMX), ciprofloxacin (CIP) and sulfamethoxazole (SMX) from aqueous solution. Adsorption kinetics, isotherms and thermodynamic parameters were systematically investigated at a solution pH of 7. Single adsorption kinetics findings suggest that experimental data for AMX, CIP and SMX was better fitted by the nonlinear pseudo-first order model with calculated maximum adsorption capacities of 23.1 mg.g−1, 23.7 mg.g−1 and 25.17 mg.g−1, respectively. Moreover, findings from the Weber-Morris kinetic model suggest that multiple processes were limiting the overall adsorption rate of AMX, CIP and SMX on chitosan-CNT. Adsorption isotherm results indicated that single adsorption experimental data was better fitted by the nonlinear Freundlich isotherm model, while binary and ternary system experimental data were better fitted by the nonlinear competitive extended Sips adsorption isotherm models. Moreover, results for binary and ternary adsorption showed that multicomponent adsorption systems exhibited both antagonistic and synergistic adsorption of AMX, CIP and SMX. From the thermodynamics fundings, it was evident that the adsorption of AMX, CIP and SMX from solution is an endothermic process governed by both physical and chemical adsorption mechanisms. Based on the findings of the current study, it was concluded that chitosan-CNT has potential as a green technology for the removal of antibiotics from solution.

Calorimetric study of the enthalpy of mixing in the liquid systems Li-Zn and Li-Sn-Zn

Partial and integral molar enthalpies of mixing of binary liquid Li-Zn alloys and ternary liquid Li-Sn-Zn alloys were determined by drop calorimetry at 823 K. The binary system was investigated up to xZn = 0.85 and the integral mixing enthalpy was described with a Redlich-Kister-polynomial. Li-Zn shows an exothermic behavior and a minimum molar mixing enthalpy of about ΔHmix = -11.7 kJ⋅mol−1 at xZn = 0.6. The mixing enthalpy along nine ternary sections of Li-Sn-Zn (A: xLi/xSn ≈ 9:11, B: xLi/xSn ≈ 1:4, C: xSn/xZn ≈ 9:1, D: xSn/xZn ≈ 7:3, E: xSn/xZn ≈ 1:1, F: xLi/xZn ≈ 1:3, G: xLi/xZn ≈ 1:1, H: xLi/xZn ≈ 3:2, I: xLi/xZn ≈ 7:3) was investigated. Solid pieces of Li, Sn, and Zn respectively were dropped to the liquid binary starting alloys in the calorimeter at 823 K. The integral values at the crossing points of the various sections show excellent agreement. The course of the partial and integral enthalpy values along the sections A, D, E, F, G, H, and I indicate multiple-phase regions. The occurrence of such partially liquid and de-mixed liquid regions is discussed and compared with a calculated version of the isothermal section at 823 K which exhibits a large ternary liquid miscibility gap. The experimental ternary data allocated to the fully liquid monophasic regions was numerically fitted based on a Redlich-Kister-Muggianu polynomial for substitutional solutions including ternary interactions. In addition, a comparison with enthalpy values from the extrapolation methods based on binary data according to the Muggianu and Toop model, respectively, is shown. Both models fail to accurately reproduce the experimental values. Iso-enthalpy plots of calculated integral mixing enthalpy for stable and metastable fully homogeneous liquid alloys for the entire ternary system are shown.

Hypoeutectic Liquid-Solid Phase Diagrams for Ternary Lithium-Ion Battery Electrolytes.

One of the current efforts of Li-ion battery technology research is to enable the operation of lithium-ion batteries in low temperature environments (<0 °C). However, the ternary phase diagrams of liquid electrolytes commonly used in lithium-ion cells are generally unknown. We herein study the liquidus surface of a ternary liquid electrolyte─up to 1 m LiPF6 in ethylene carbonate and ethyl methyl carbonate. We find that literature electrochemical and thermodynamic measurements of the composing binary electrolytes, in addition to limited ternary liquidus data, can be used to recover the liquidus surface via appropriate mixing terms.

Phase equilibria and mechanism analysis of separating ethanol from fuel additives by chord chloride-deep electrochemical solvents

There is an azeotropic mixture of ethanol and heptane in the production process of gasoline additives. This work explored the possibility of extracting ethanol from ethanol and heptane with deep eutectic solvents (DESs) to recover components and protect the environment. Three kinds of choline chloride (ChCl)-DESs were synthesized, and then the ternary phase equilibrium data of ethanol, heptane and DESs were measured. The reliability of the data can be verified by the fitting results of the experimental data and the linear equation. In addition, the NRTL model can be correlated with the phase equilibrium results, and the accuracy of the fitted data is determined by the GM/RT surface of GUI-MATLAB. The calculated root mean square deviation shows that the phase equilibrium data has high accuracy. The experimental data show that ChCl-ethylene glycol DES have better ethanol separation results. Then the microscopic mechanism of the separation process was analyzed. The analysis results of electrostatic potential and independent gradient model show that the interaction between DESs and ethanol is mainly hydrogen bond interaction and van der Waals interaction, among which hydrogen bond interaction is the main interaction. In addition, the effect of DES structure on ethanol separation was revealed by the microscopic distribution of molecular dynamics simulation. This work provides a theoretical reference for selecting DESs to separate ethanol-heptane azeotrope.

Ternary combinational logic gate design based on tri-valued memristors

Traditional binary combinational logic circuits are generally obtained by cascading multiple basic logic gate circuits, using more components and complicated wiring. In contrast to the binary logic circuit design in this method, ternary combinational logic circuit implementation is more complicated. In this paper, a ternary circuit design method that does not require cascading basic ternary logic gates is proposed based on a tri-valued memristor, which can directly realize specific logic functions through a series connection of memristors. The ternary encoder, ternary decoder, ternary comparator, and ternary data selector are implemented by this method, and the effectiveness of the circuits is verified by LTspice simulations.

Thermodynamic and kinetic assessments in Co–Cr–Mn system using diffusion data

Atomic mobility and thermodynamic assessments of the fcc Co–Cr–Mn system were performed using the ternary diffusion data. Diffusion couples in the Co–rich region of the ternary fcc Co–Cr–Mn system were prepared and annealed at 900, 1050, and 1150 °C. The concentration profiles across the joint interface were measured using an electron probe microstructure analysis technique. The interdiffusivities were evaluated using the Whittle–Green method, and the atomic mobility parameters of the fcc phase were evaluated via numerical analysis of the diffusional flux. The thermodynamic parameters of the binary fcc Cr–Mn alloy were also determined using the CALPHAD method during the assessment of the mobility parameters in the Co–Cr–Mn system. The atomic mobility and thermodynamic descriptions obtained in this study were used to simulate the ternary concentration profiles. The excellent agreement between the experimental and simulated diffusion data validates the atomic mobility and thermodynamic parameters evaluated in this study.Graphical

VLE measurement of binary systems 1-butyl-3-methylimidazolium-based ionic liquids and ethanol or water

Experimental vapor–liquid equilibrium (VLE) data for binary systems of ethanol or water with four ionic liquids (ILs) comprising 1-butyl-3-methylimidazolium cation and different anions were measured at atmospheric pressure within a broad region of IL concentrations, IL mole fraction ranging between 0 and 0.7 or even 0.87. Siwoloboff procedure of vapor–liquid phase transition temperature measurement was applied to estimate VLE of the binary mixtures. Obtained t–x data were fitted assuming ideal vapor and non-ideal liquid phase behavior, using the original NRTL equation to calculate component activity coefficients. VLE description results are presented in form of equilibrium diagrams and as the variation of activity coefficients with mixture composition. Quality of the VLE data fitting was assessed based on root mean square deviations and mean deviations in temperature. Some experimental binary t–x data sets were compared to those available in literature (measured in much narrower concentration range) or predicted from ternary VLE data. The selection of ILs in this study was related to the separation of ethanol−water mixture by extractive distillation. Principal IL selection criterium was its ability to affect the ethanol–water relative volatility. Thermodynamic description of the VLE data of IL-containing systems within a wide concentration range is an essential building block of the mathematical model of separation devices, especially in case of regeneration equipment where concentrated IL is present.

Correlating the Density and Refractive Index of Ternary Liquid Mixtures

Some physical properties of ideal solutions, e.g. the molar volume and the molar refraction, vary linearly with composition. Others can be expressed, either as ratios or as products of two other properties which vary with composition in this way. It is postulated that the non-ideal behaviour of real solutions can be adequately modelled by substituting these linear functions with higher order Scheffé polynomials. A suite of such models is presented for which the parameters are fully determined by knowledge of pure component properties and binary behaviour. Their binary data representation ability, and capacity to predict ternary properties, was tested using density and refractive index data for the acetic acid–ethanol-water ternary system as well as fourteen additional ternary data sets. Model performance was ranked on the basis of the Akaike Information criterion. With respect to predicting ternary density and refractive index behaviour from knowledge of binary data, it was found that lower-order models outperformed higher order models.

Surface tension of binary and ternary mixtures mapping with ASP and UNIFAC models based on machine learning

Modeling predictions of surface tension for binary and ternary liquid mixtures is difficult. In this work, we propose a machine learning model to accurately predict the surface tension of binary mixtures of organic solvents-ionic liquids and ternary mixtures of organic solvents-ionic liquids–water and analytically characterize the proposed model. In total, 1593 binary mixture data points and 216 ternary mixture data points were collected to develop the machine learning model. The model was developed by combining machine learning algorithms, UNIFAC (UNIversal quasi-chemical Functional group Activity Coefficient) and ASP (Abraham solvation parameter). UNIFAC parameters are used to describe ionic liquids, and ASP is used to describe organic solvents. The effect of each parameter on the surface tension is characterized by SHAP (SHapley Additive exPlanation). We considered support vector regression, artificial neural network, K nearest neighbor regression, random forest regression, LightGBM (light gradient boosting machine), and CatBoost (categorical boosting) algorithms. The results show that the CatBoost algorithm works best, MAE = 0.3338, RMSE = 0.7565, and R2 = 0.9946. The SHAP results show that the surface tension of the liquid decreases as the volume and surface area of the anion increase. This work not only accurately predicts the surface tension of binary and ternary mixtures, but also provides illuminating insight into the microscopic interactions between physical empirical models and physical and chemical properties.

Liquid-liquid equilibria of the ternary systems water + C1 – C3 alcohols + dimethyl adipate at 298.15 K and atmospheric pressure: Experimental data and modeling

The aim of this study was to examine the possibility of using a green organic solvent, dimethyl adipate (DMA), as a separation agent in the extraction of different alcohols from aqueous solutions. For that purpose, the liquid–liquid equilibria (LLE) of four ternary systems were analytically determined at 298.15 K and atmospheric pressure using stirred and thermo-regulated cells: {water + methanol + dimethyl adipate (DMA)}, {water + ethanol + DMA}, {water + 1-propanol + DMA}, {water + 2-propanol + DMA}. Phase diagrams for these four ternary systems were obtained by determining a binodal curve and tie-lines data using a visual cloud-point method, but also gas chromatography. Distribution coefficients and separation factors were calculated for the immiscibility region. Hand and Othmer-Tobias correlations were used to examine the reliability of the tie-line data. In addition, the experimental ternary LLE data were correlated with the UNIversal QUAsiChemical (UNIQUAC) activity coefficient models. The obtained results showed that DMA is a suitable candidate for the extraction of methanol, ethanol, 1-propanol, and 2-propanol from water. With some alcohols, this potential is more noticeable than with others. The potential of DMA as a separation agent is increasing in the following sequence: methanol < ethanol < 2-propanol < 1-propanol, and that also could be noticed from the selectivity values that also increase in this sequence. The UNIQUAC model proved as successful in correlating the LLE data.

A Digital Processing in Memory Architecture Using TCAM for Rapid Learning and Inference Based on a Spike Location Dependent Plasticity

In this paper, we present a digital processing in memory (DPIM) configured as a stride edge-detection search frequency neural network (SE-SFNN) which is trained through spike location dependent plasticity (SLDP), a learning mechanism reminiscent of spike timing dependent plasticity (STDP). This mechanism allows for rapid online learning as well as a simple memory-based implementation. In particular, we employ a ternary data scheme to take advantage of ternary content addressable memory (TCAM). The scheme utilizes a ternary representation of the image pixels, and the TCAMs are used in a two-layer format to significantly reduce the computation time. The first layer applies several filtering kernels, followed by the second layer, which reorders the pattern dictionaries of TCAMs to place the most frequent patterns at the top of each supervised TCAM dictionary. Numerous TCAM blocks in both layers operate in a massively parallel fashion using digital ternary values. There are no complicated multiply operations performed, and learning is performed in a feedforward scheme. This allows rapid and robust learning as a trade-off with the parallel memory block size. Furthermore, we propose a method to reduce the TCAM memory size using a two-tiered minor to major promotion (M2MP) of frequently occurring patterns. This reduction scheme is performed concurrently during the learning operation without incurring a preconditioning overhead. We show that with minimal circuit overhead, the required memory size is reduced by 84.4%, and the total clock cycles required for learning also decrease by 97.31 % while the accuracy decreases only by 1.12%. We classified images with 94.58% accuracy on the MNIST dataset. Using a 100 MHz clock, our simulation results show that the MNIST training takes about 6.3 ms dissipating less than 4 mW of average power. In terms of inference speed, the trained hardware is capable of processing 5,882,352 images per second.

Optically Controllable Organic Logic-in-Memory: An Innovative Approach toward Ternary Data Processing and Storage.

Logic-in-memory (LIM) has emerged as an energy-efficient computing technology, as it integrates logic and memory operations in a single device architecture. Herein, a concept of ternary LIM is established. First, a p-type 2,7-dioctyl[1]benzothieno[3,2-b][1]benzothiophene (C8-BTBT) transistor is combined with an n-type PhC2H4-benzo[de]isoquinolino[1,8-gh]quinolone diimide (PhC2-BQQDI) transistor to obtain a binary memory inverter, in which a zinc phthalocyanine-cored polystyrene (ZnPc-PS4) layer serves as a floating gate. The contrasting photoresponse of the transistors toward visible and ultraviolet light and the efficient hole-trapping ability of ZnPc-PS4 enable us to achieve an optically controllable memory operation with a high memory window of 18 V. Then, a ternary memory inverter is developed using an anti-ambipolar transistor to achieve a three-level data processing and storage system for more advanced LIM applications. Finally, low-voltage operation of the devices is achieved by employing a high-k dielectric layer, which highlights the potential of the developed LIM units for next-generation low-power electronics.

Ternary liquid–liquid equilibria for 2-phenylethyl acetate + 2-phenylethanol + water and 2-phenylethyl acetate + acetic acid + water at atmospheric pressure

2-Phenylethyl acetate can normally be produced by the esterification of 2-phenylethanol with acetic acid via a reactive distillation process. The multiphase coexistence behavior is a component of esterification and is important for the design of the reactive distillation process. In this report, the ternary liquid–liquid equilibrium (LLE) results of 2-phenylethyl acetate + 2-phenylethanol + water and 2-phenylethyl acetate + acetic acid + water at 303.15, 323.15, and 343.15 K under atmospheric pressure are presented. The ternary LLE data regression results were obtained using NRTL and UNIQUAC models with an absolute average deviation (AAD%) value of less than 2 %. The determined binary interaction parameters were verified by the GUI-MATLAB tool. Furthermore, the results predicted by the UNIFAC model for the investigated ternary LLE systems are acceptable.

Generic bond energy formalism within the modified quasichemical model for ternary solutions

The Modified Quasichemical Model in the Pair Approximation (MQMPA) can effectively capture the thermodynamic features of a binary solution with Short-Range Ordering (SRO). If the model is used to treat a ternary solution, a geometric interpolation method must be employed to extend the bond energy expression from binary to ternary formalism. The aim of the present work is to implement such extension by means of a generic geometric interpolation approach. The generic method is unbiased and can be transformed into the widely used Kohler, Toop and Muggianu approaches with special interpolation parameters. The interpolation parameters can be calculated by the integration method as well as be optimized by ternary experimental data. The generic bond energy formalism (GBEF) has thus been derived to provide the MQMPA great flexibility to describe ternary solutions with complex configurations. Moreover, the GBEF is more concise than the formula derived by a combinatorial Kohler-Toop method. The concise GBEF is in the respect more conveniently programmed. Eventually, the Cu-Li-Sn liquid where both SRO and clustering among atoms occur is employed to validate the effectiveness and reliability of the GBEF within the MQMPA.

Multiple cryoprotectant toxicity model for vitrification solution optimization

Vitrification is a promising cryopreservation technique for complex specimens such as tissues and organs. However, it is challenging to identify mixtures of cryoprotectants (CPAs) that prevent ice formation without exerting excessive toxicity. In this work, we developed a multi-CPA toxicity model that predicts the toxicity kinetics of mixtures containing five of the most common CPAs used in the field (glycerol, dimethyl sulfoxide (DMSO), propylene glycol, ethylene glycol, and formamide). The model accounts for specific toxicity, non-specific toxicity, and interactions between CPAs. The proposed model shows reasonable agreement with training data for single and binary CPA solutions, as well as ternary CPA solution validation data. Sloppy model analysis was used to examine the model parameters that were most important for predictions, providing clues about mechanisms of toxicity. This analysis revealed that the model terms for non-specific toxicity were particularly important, especially the non-specific toxicity of propylene glycol, as well as model terms for specific toxicity of formamide and interactions between formamide and glycerol. To demonstrate the potential for model-based design of vitrification methods, we paired the multi-CPA toxicity model with a published vitrification/devitrification model to identify vitrifiable CPA mixtures that are predicted to have minimal toxicity. The resulting optimized vitrification solution composition was a mixture of 7.4 molal glycerol, 1.4 molal DMSO, and 2.4 molal formamide. This demonstrates the potential for mathematical optimization of vitrification solution composition and sets the stage for future studies to optimize the complete vitrification process, including CPA mixture composition and CPA addition and removal methods.

Research on multi-factory combination optimization based on DOSTAR

With the development of industrial big data, it has become an important research direction to use combinatorial optimization to coordinate multi-objective problems in complex manufacturing scenarios with multiple factories. At present, most of the multi-objective problems are decomposed into single-objective solutions. However, it is difficult to resolve the contradiction between multiple goals. There are many participants in multi-objective problems and complex data types, so there is no suitable research method at present. Based on big data, this paper integrates various aspects of supply chain management of multiple factories, and proposes a DOSTAR combined model. On the one hand, it conducts knowledge discovery based on the fusion of human-cyber-physical ternary data, on the other hand, it conducts multi-objective optimization through knowledge structure. Among them, the most important thing is to establish the six-tuple as the basic model. Then the space weight, time weight and decision weight are obtained through the weight sub-model. Finally, the improved reinforcement learning algorithm is used to extract relevant new knowledge and complete multi-objective coordination. This article takes the supply chain management of Haier water heaters as an example, using the above-mentioned combined model, and the experimental results show that the purpose of improving performance has been achieved. • This article builds a combined optimization model based on knowledge discovery and reinforcement learning. • The combined model mainly includes: six tuples, weight sub-models (AHP and spatiotemporal data), and improved reinforcement learning algorithms. • This research solves the multi-objective optimization problem in the complex manufacturing environment of multi-factories.