Semi-empirical Model Research Articles

In vitro dissolution of encapsulated or dispersed vitamin E and cholesterol is correlated with preservation of refrigerated ovine sperm

BACKGROUND: Vitamin E (α‐tocopherol) and cholesterol are crucial components in cellular protection and physiological processes. Their uses in biological media face challenges due to their poor solubility and stability. OBJECTIVE: The study investigated the complex interactions of these bioactive compounds in various encapsulation systems of cyclodextrin and liposome, as well as dispersion in PEG-6000, in an attempt to improve the viability, motility, and preservation of ovine sperm cells. MATERIALS AND METHODS: The work explored the in vitro dissolution kinetics of vitamin E (α‐tocopherol) and cholesterol using semi-empirical models. RESULTS: The release profiles of VitE and Chl varied considerably, depending on the specific carrier systems. For liposome-loaded VitE and Chl, the Korsmeyer-Peppas model gave the best fit; for CD/VitE and CD/Chl, the Higuchi model provided the best fit, whereas for PEG-6000 dispersions (VitE and Chl) both the Higuchi and Korsmeyer-Peppas models demonstrated the excellent fit. All systems indicated a Fickian diffusion mechanism dictated by the concentration gradient. The delivery of VitE and Chl with CD, liposome and PEG dispersion significantly increased sperm mobility and motility. The effect on the VCL parameter was the greatest by liposome-loaded VitE and Chl, followed by CD encapsulation and PEG-6000 dispersion. CONCLUSION: The dynamics of vitamin E and cholesterol within innovative delivery systems offers valuable insights into the development of advanced solutions in reproductive health, particularly on improving the viability, motility of refrigerated ovine sperm cells.

Modeling of nonlinear self-excited forces: A study on flutter characteristics and mechanisms of post-flutter behaviors

The intrinsic physical relevance of higher-order self-excited force (SEF) components has received limited attention, and there is a dearth of formulas that adequately analyze the influence of SEF components on the post-flutter characteristics. Based on Taylor formulas and the principle of independence, semi-empirical polynomial SEF models are developed and validated. The energy input efficiency and role of each order SEF component are examined using the proposed models. By introducing the principle of energy equivalence and approximate average power, theoretical formulas designed to calculate the post-flutter characteristics are established. Finally, the applicability and robustness of the SEF models and theoretical formulas are discussed. Results show that the proposed models can obtain independent higher-order SEF components, which is conducive to the correct analysis of the SEF driving mechanisms. The theoretical formulas can accurately reconstruct the time-varying curves of the flutter characteristics, and the terms in the formulas can explicitly calculate and analyze the mechanism of each SEF model element. It is observed that the higher-order SEF components have a significant impact on the accurate reconstruction of SEFs while barely affecting the system energy. Moreover, the limit cycle oscillation generation mechanisms of the investigated two rectangular cylinders are different, but the variation of the flutter characteristics with time remain the same.

Kinetic modelling of UVC and UVC/H2O2 oxidation of an aqueous mixture of antibiotics in a completely mixed batch photoreactor

The removal kinetics of an aqueous mixture of thirteen antibiotics (i.e., ampicillin, cefuroxime, ciprofloxacin, flumequine, metronidazole, ofloxacin, oxytetracycline, sulfadimethoxine, sulfamethoxazole, sulfamethazine, tetracycline, trimethoprim and tylosin) by batch UVC and UVC/H2O2 processes has been modeled in this work. First, molar absorption coefficients (ε), direct quantum yields (Φ) and the rate constants of the reaction of antibiotics with hydroxyl radical (kHO•) (model inputs) were determined for each antibiotic and compared with literature data. The values of these parameters range from 0.3 to 21.8 mM−1 cm−1 for ε, < 0.01 to 67.8 mmol·E−1 for Φ and 3.8 × 109 to 1.7 × 1010 M−1 s−1 for kHO•. Second, a regression model was developed to compute the rate constants of the reactions of the antibiotics with singlet oxygen (k1O₂) from experimental data obtained in batch UVC experiments treating a mixture of the antibiotics. k1O₂ values in the 1–50 × 106 M−1 s−1 range were obtained for the antibiotics studied. Finally, a semi-empirical kinetic model comprising a set of ordinary differential equations was solved to simulate the evolution of the residual concentration of antibiotics and hydrogen peroxide (model outputs) in a completely mixed batch photoreactor. Model predictions were reasonably consistent with the experimental data. The kinetic model developed might be combined with computational fluid dynamics to predict process performance and energy consumption in UVC and UVC/H2O2 applications at full scale.

Assessment of a PAH-based soot production model in laminar coflow methane diffusion flames doped by gasoline surrogate fuels

This article assesses the capability of the PAH-based soot model developed by the authors and validated in ethylene non-premixed flames to predict soot production in flames fueled with gasoline surrogates. The soot model was coupled to a flamelet model and the Rank-Correlated Full-Spectrum k model to simulate laminar coflow nitrogen-diluted methane/air diffusion flames doped with n-heptane/toluene and iso-octane/toluene mixtures. Consistent with our previous studies, the simulation was conducted using the Kaust Mechanism 1, pyrene as soot precursor, and the same set of model parameters. The model reproduced reasonably-well the peak soot volume fraction. However, the soot production onset was predicted much earlier than measurements owing to the early formation of pyrene induced by the presence of toluene. These discrepancies can be partially corrected by selecting a larger PAH than pyrene with a similar level of concentrations as soot precursor. For the present mechanism, anthanthrene was found to be the best candidate. Model results show that different mechanisms dominate the soot mass growth in ethylene and gasoline surrogate flames. While the HACA is more important in the former, PAH condensation largely prevails in the latter. This suggests that ethylene may be not the most relevant reference fuel for developing semi-empirical soot models for fires. Further investigations are required to confirm this conjecture.

Hysteresis in strongly magnetized N2 discharges

A semi-empirical global model for a nitrogen discharge in a strong magnetic field is developed. The model is based upon experimental data from high-resolution Doppler and extreme-ultraviolet vacuum spectroscopy, which establish the plasma composition, discharge parameters, and, most importantly, electronic transitions. This allows the number of required molecular systems and atomic/ionic states to be reduced, thereby retaining only the essential plasma chemistry reactions. The set of 35 stiff non-linear ordinary differential equations is numerically integrated using an unconditionally stable adaptive method. Simulations show the existence of two solution branches with low and high electron temperature, respectively. A distinct hysteresis is exhibited by the discharge and illustrated for three typical N2 mass flow rates. The dependencies of the plasma parameters on the applied power are presented and discussed in detail, including in the vicinity of the bifurcation points. The efficiency of operation in the opposing limits of N2 discharge behavior as either a source of plasma or light emission is examined, with special emphasis on electric propulsion capabilities.

A composite model for nucleate boiling heat transfer in a slush nitrogen pool

The pool boiling of slush nitrogen (SlN2) with various solid volume fractions at subatmospheric pressures is studied to establish a semi-empirical model considering the effects of phase-change solid nitrogen (SN2) particles on convective heat flux and boiling heat flux. The convective heat flux is studied with the modified correlation based on Sindt's fitting, while an improved correlation based on Rohsenow correlation with the dimensionless form of wall superheat in the range of 0<Ja∗ρ∗0.14<0.04 is obtained for the boiling heat flux. With the increasing wall superheat, the enhancement weakens till vanishes at the transition point. This point marks the separation between low- and high-heat-flux regions, where the probability of SN2 invading to thermal boundary layer diminishes to 0 %. The low-heat-flux boiling correlation considers the heat transfer enhancement due to SN2 concentration while the high-heat-flux one ignores the impact of SN2. As the components of SlN2 nucleate boiling model, the convective and boiling heat flux both show the accuracy of ±20 % with experiments. Upon analyzing the boiling curves of SlN2 with various solid volume fractions, SlN2 outperforms LN2 in coolant efficiency at moderate heat load.

A semiempirical model for low energy electron–atom transport cross sections: The case of noble gases

A semiempirical approach to describe low energy electron–atom transport cross sections of easy implementation and reproduction is presented. The heart of the model is an energy independent two-parameter potential that was adjusted to reproduce the accurate total cross sections for He, Ne, Ar and Kr, measured with a threshold photoelectron source technique from meV up to 20 eV. Once the potential was conceived, the model was validated by comparing the values obtained for the calculated scattering lengths and phase shifts with the respective quantities previously reported in the literature. We close the article by presenting the momentum transfer and viscosity cross sections. Good agreement is found when compared to the similar data obtained from swarm experiments, from phase shifts according to differential cross section measurements and to the cross sections reported by sophisticated ab initio relativistic many-body calculations. Tables for the phase shifts and cross sections are provided for direct use and applications.

Comparative analysis of machine learning techniques for accurate prediction of unfrozen water content in frozen soils

Unfrozen water content (UWC) plays a critical role in determining the thermal, hydraulic, and mechanical properties of frozen soils. Existing empirical, semi-empirical, and theoretical models for UWC estimation have limitations in terms of accuracy as well as generalizability. To address these challenges, the present study explored the application of six machine learning techniques to predict UWC in frozen soils: Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), K-Nearest Neighbors (KNN), Support Vector Regression (SVR), and Backpropagation Neural Network (BPNN). Considering the UWC hysteresis phenomenon between the freezing and thawing processes, experimental UWC data collected from the literature were separated into two sub-datasets: freezing branch dataset (FBD) and thawing branch dataset (TBD). Based on that, a comprehensive framework integrating Bayesian optimization and 10-fold cross-validation was established to optimize the six models' hyperparameters and to evaluate their performance. The results highlighted significant variations in the predictive capability among the six machine learning models, with ensemble methods (i.e., RF, XGBoost, LightGBM) generally demonstrating superior accuracy. Feature importance analysis, robustness checks, and uncertainty quantification further elucidated the strengths and limitations of each model. The present study provides profound insights into the selection and application of machine learning models for accurately modeling the properties of frozen soils for cold regions science and engineering.

Innovative approaches to overcome inadequate measurements in heat pumps with non-fluorinated refrigerants

As the transition away from fluorinated refrigerants occurs due to F-gas and PFAS regulations, heat pumps face the challenge of adapting to new non-fluorinated refrigerants. Evaluating heat pump performance during this transition is challenging due to limited operational data on the new refrigerants. Conducting long-term tests to fully understand a heat pump’s performance with all possible refrigerants is labor-intensive and economically burdensome. This study introduces two complementary reduced-parameter models to assess heat pump performance across multiple new natural refrigerants despite limited data. A transfer learning model, leveraging knowledge from existing data-rich refrigerants, has been developed to evaluate the performance of heat pumps using new, data-scarce natural refrigerants. However, due to the lack of transparency in transfer learning models, semi-empirical models are being developed in parallel. The semi-empirical models, across multiple natural refrigerants, are capable of analyzing the thermodynamics and heat transfer processes within the heat pump system by utilizing only limited easy-to-measure variables as inputs. The transfer learning model demonstrates high accuracy for all outputs across seven refrigerants with RRMSE all below 7%. In comparison, the semi-empirical models are less accurate, with RRMSE results under 25% for all parameters except compressor power. By integrating these two models, a comprehensive framework is established for assessing heat pump performance with both high accuracy and a deeper understanding of the system.

A simple and efficient approach for preparing cationic coating with tunable electroosmotic flow for capillary zone electrophoresis-mass spectrometry-based top-down proteomics

BackgroundCapillary zone electrophoresis-tandem mass spectrometry (CZE-MS/MS) has become a valuable analytical technique in top-down proteomics (TDP). CZE-MS/MS-based TDP typically employs separation capillaries with neutral coatings (i.e., linear polyacrylamide, LPA). However, issues related to separation resolution and reproducibility remain with the LPA-coated capillaries due to the unavoidable non-specific protein adsorption onto the capillary wall. Cationic coatings can be critical alternatives to LPA coating for CZE-MS/MS-based TDP due to the electrostatic repulsion between the positively charged capillary inner wall and proteoform molecules in the acidic separation buffer. Unfortunately, there are only very few studies using cationic coating-based CZE-MS/MS for TDP studies. ResultsIn this work, we aimed to develop a simple and efficient approach for preparing separation capillaries with a cationic coating, i.e., poly (acrylamide-co-(3-acrylamidopropyl) trimethylammonium chloride [PAMAPTAC]) for CZE-MS/MS-based TDP. The PAMAPTAC coating-based CZE-MS produced significantly better separation resolution of proteoforms compared to the traditionally used LPA-coated approach. It achieved reproducible separation and measurement of a simple proteoform mixture and a complex proteome sample (i.e., a yeast cell lysate) regarding migration time, proteoform intensity, and the number of proteoform identifications. The PAMAPTAC coating-based CZE-MS enabled the detection of large proteoforms (≥30 kDa) from the yeast cell lysate reproducibly without any size-based prefractionation. Interestingly, the mobility of proteoforms using the PAMAPTAC coating can be predicted accurately using a simple semi-empirical model. SignificanceThe results render the PAMAPTAC coating as a valuable alternative to the LPA coating to advance CZE-MS-based TDP towards high-resolution separation and highly reproducible measurement of proteoforms in complex samples.

Assessment of a NaOH-based alkaline electrolyser’s performance: System modelling and operating parameters optimisation

Most of the scientific research is focused on KOH-based alkaline electrolysers, while NaOH-based ones are unexplored although they present interesting features. This paper presents a semi-empirical model developed in the Python environment to predict a NaOH-based alkaline electrolyser’s performance to cover such a research gap and perform an optimisation procedure of electrochemical parameters. A sensitivity analysis has been carried out to study how its performance changes while varying the: i) NaOH content, ii) pressure, and iii) both. Separately, the best result has been obtained with a NaOH content and an operating pressure of 8% and 6.5 bar, respectively. Furthermore, the same values have been recorded even by varying both the NaOH content and the operating pressure. Specifically, a maximum average efficiency increase of 3.57% at 35°C, 0.17% at 40 °C, and 3.74% at 35°C in the case of NaOH content, pressure, and both, respectively.

Non-uniform distribution model of rust layer around steel bar circumference and its effect on corrosion-induced concrete cracking

This paper presents a critical review of non-uniform distribution models of rust layer around a reinforcing bar’s circumference and a numerical investigation into the effect of non-uniform distribution of rust on (a) crack patterns and (b) development of concrete surface crack width. Different shapes of rust layer simulated by mathematical models were established based on statistical distribution functions which were compared with the transport-electrochemical model and published testing results. In addition, an advanced 2D finite element model was implemented to simulate crack propagation in concrete caused by rebar corrosion. It is found that non-uniform rust distribution shape around the rebar not only affected concrete surface crack width evolution, but also crack length and crack pattern. Transport-electrochemical model and Gaussian distribution-based model could reasonably simulate rust distribution and associated corrosion-induced cracks. In addition, the authors proposed a simplified semi-empirical model that incorporated an analytical chloride transport model and a Gaussian model to simulate time-dependent advancement of non-uniform rust layer. Furthermore, a parametric study of the effect of elastic stiffness of the rust layer on crack patterns and surface crack width development was also performed.

Fast and accurate modelling of twin-screw compressors: A generalised low-order approach

Twin-screw compressors (TSC) are commonly used in heat pump processes due to their robustness and flexibility. They exhibit two core properties, i.e. the swept volume and the built-in volume ratio (BVR), which heavily influence their capacity limits and off-design efficiency. This work presents a new low-order model (i.e. a polynomial model), which can accurately predict a TSC’s behaviour. The model uses the external pressure ratio and volumetric compressor inlet flow rate to calculate isentropic efficiency and compressor speed. The input parameters are normalised with a reference flow rate (calculated from the swept volume) and the BVR, respectively. This results in a generalised model of low numerical cost, which can be used for explorative studies independent of the specific machine size and BVR. A gain in computational speed by a factor of 375 is achieved compared to a semi-empiric reference model. The model displays very good predictive accuracy when used to predict the performance of machines with similar BVRs, but different sizes. A mean deviation from the manufacturer data of 4.29%, 0.88°C and 1.38% for the shaft power, the outlet temperature and the compressor speed can be observed, respectively. When there is a difference in size and BVR, the prediction accuracy is still reasonable but significantly declines for small and very large pressure ratios. Nevertheless, the proposed new approach extends the state-of-the-art by introducing a low-order model, which combines the advantages of low computational cost, high accuracy, physically correct predictions over a wide operational range and scalability to different machine capacities and BVRs. The validation for different fluids indicates a good general prediction accuracy relatively independent of the used fluid.

Semi-empirical model of a copper bromide vapor brightness amplifier

We propose the semi-empirical mathematical model of the brightness amplifier based on the self-terminating transitions in laser active media. The model is applied to the copper bromide vapor brightness amplifier. The feature of the model is that it allows one to distinguish the amplified spontaneous emission and amplified input optical signal. Due to that we have managed to study the “real” gain and theoretical maximum of contrast for the brightness amplifier depending on temporal and energy characteristics of the input optical signal.

Experimental and theoretical investigation of CO2 solubility in amine-based deep eutectic solvents

Deep eutectic solvents (DESs) have appealed increasing research interest across various scientific and industrial applications, notably in relation to efforts in CO2 capture. This study presents a novel series of DESs were synthesized on the basis of monoethanolamine (MEA) and N-methyldiethanolamine (MDEA) as hydrogen bond donors with tetrabutylammonium bromide (TBAB) and tetrabutylphosphine bromide (TBPB) salt as hydrogen bond acceptors. A gas solubility measurement experimental system based on pressure drop method has been established to evaluate the solubility of CO2 in amine-based deep eutectic solvents. The evaluation was performed over a temperature range of 303.15 to 323.15 K and a pressure range of 100 to 1000 kPa. The results indicated that as the molar ratio of hydrogen bond donors increases, the solubility of CO2 in DESs increases, while it was found to be less affected by the hydrogen bond acceptor. Of these, the solubility of CO2 is strongest at a 1:10 molar ratio of TBAB and MEA. The nuclear magnetic resonance spectroscopy spectra (NMR) and FTIR spectroscopy revealed that the MEA-based DES mixture primarily exhibited chemical absorption of CO2, while the MDEA-based DES predominantly showed physical dissolution of CO2. In addition, it was observed that both temperature and pressure significantly influenced the CO2 solubility, and a successful correlation was developed using different semi-empirical models.

Design and characterisation of an open-jet pressure gradient test rig for an aeroacoustic wind tunnel

This paper summarises an approach to generating controllable pressure gradients within an open-jet configuration for aeroacoustics research. A novel open-jet pressure gradient test rig has been designed for the UNSW Anechoic Wind Tunnel with the help of RANS simulations. A range of pressure gradients is created by adjusting the inclination angle of the top plate to change the cross-sectional area along the streamwise direction gradually. The test model mounting point is located on the bottom plate adjacent to partially opened side walls to allow far-field noise measurements. A comprehensive characterisation of flow quality, pressure gradient parameters and acoustic data quality has been carried out using Particle Imaging Velocimetry (PIV), surface pressure taps, and a microphone array. The test rig produces near-uniform pressure gradient flows at the model mounting point, with momentum Reynolds numbers (Reθ) ranging from 3014 to 11853 and Clauser's pressure gradient parameters (β) from -0.24 to 1.66. The pressure gradients generated by this facility are approximately linear, approaching the model mounting point, and the boundary layer profiles compare favourably with those from conventional hard-walled enclosed pressure gradient wind tunnel facilities. Measurements of airfoil trailing-edge noise from this test rig compare well with classical semi-empirical model predictions. Simultaneous acoustic and flow measurements on a square finite wall-mounted cylinder showcase the capability of this facility for coupled acoustic-flow diagnosis.

Fabrication of electrospun ion exchanger adsorbents with morphologies designed for the separation of proteins and plasmid DNA

Electrospun cellulose adsorbents are an emergent class of materials applied to a variety of bioprocess separations as an analogue to conventional packed bed chromatography. Electrospun adsorbents have proven to be effective as rapid cycling media, enabling high throughput separation of proteins and viral vectors without compromising selectivity and recovery. However, there is a current lack of knowledge in relation to the manipulation and control of electrospun adsorbent structure with function and performance to cater to the separation needs of emerging, diverse biological products.In this study, a series of electrospun cellulose adsorbents were fabricated by adjusting their manufacturing conditions. A range of fiber diameters (400 to 600 nm) was created by changing the electrospinning polymer solution. Additionally, a range of porosities (0.4 to 0.7 v/v) was achieved by varying the laminating pressures on the electrospun sheets. The adsorbents were functionalized with different degrees of quaternary amine ligand density to create 18 prototype anion exchangers. Their morphology was characterized by BET nitrogen adsorption surface area, X-ray computed tomography, capillary flow porometry and scanning electron microscopy measurements.The physical characteristics of the adsorbents were used in an adapted semi-empirical model and compared to measured permeability data. Permeabilities of prototypes ranged from 10−2 to 10−4 mDarcy. The measured data showed good adherence to modelled data with possible improvements in acquiring wet adsorbent characteristics instead of dried material. Finally, the electrospun adsorbents were characterized for their binding capacity of model proteins of different sizes (diameters of 3.5 nm and 8.9 nm) and plasmid DNA. Static binding capacities ranged from 5 mg/ml to 25 mg/ml for the proteins and plasmid DNA and showed <20 % deviation from monolayer coverage based on BET surface area. Therefore, it was concluded that the electrospun adsorbents most likely adsorb monolayers of proteins and plasmid DNA on the surface with minimal steric hindrance.

At the Limit of Interfacial Sharpness in Nanowire Axial Heterostructures.

As semiconductor devices approach dimensions at the atomic scale, controlling the compositional grading across heterointerfaces becomes paramount. Particularly in nanowire axial heterostructures, which are promising for a broad spectrum of nanotechnology applications, the achievement of sharp heterointerfaces has been challenging owing to peculiarities of the commonly used vapor-liquid-solid growth mode. Here, the grading of Al across GaAs/AlxGa1-xAs/GaAs heterostructures in self-catalyzed nanowires is studied, aiming at finding the limits of the interfacial sharpness for this technologically versatile material system. A pulsed growth mode ensures precise control of the growth mechanisms even at low temperatures, while a semiempirical thermodynamic model is derived to fit the experimental Al-content profiles and quantitatively describe the dependences of the interfacial sharpness on the growth temperature, the nanowire radius, and the Al content. Finally, symmetrical Al profiles with interfacial widths of 2-3 atomic planes, at the limit of the measurement accuracy, are obtained, outperforming even equivalent thin-film heterostructures. The proposed method enables the development of advanced heterostructure schemes for a more effective utilization of the nanowire platform; moreover, it is considered expandable to other material systems and nanostructure types.

Converging Time-Dependent Density Functional Theory Calculations in Five Iterations with Minimal Auxiliary Preconditioning.

Eigenvalue problems and linear systems of equations involving large symmetric matrices are commonly solved in quantum chemistry using Krylov space methods, such as the Davidson algorithm. The preconditioner is a key component of Krylov space methods that accelerates convergence by improving the quality of new guesses at each iteration. We systematically design a new preconditioner for time-dependent density functional theory (TDDFT) calculations based on the recently introduced TDDFT-ris semiempirical model by retuning the empirical scaling factor and the angular momenta of a minimal auxiliary basis. The final preconditioner produced includes up to d-functions in the auxiliary basis and is named "rid". The rid preconditioner converges excitation energies and polarizabilities in 5-6 iterations on average, a factor of 2-3 faster than the conventional diagonal preconditioner, without changing the converged results. Thus, the rid preconditioner is a broadly applicable and efficient preconditioner for TDDFT calculations.

Note on the low deposition rate during reactive magnetron sputtering

Previously published data on the metal partial sputter yield from a poisoned target during reactive magnetron sputtering is confronted with the metal partial sputter yield from the corresponding compound calculated based on an existing semi-empirical model validated with experimental ion beam data. Both yields are linearly correlated but the magnetron based sputter yields are approximately 8 times lower. The lower yield is explained from the formation of an oxygen rich top layer. This hypothesis is verified by binary collision approximation Monte Carlo simulations.