Experimental Measurements Research Articles

Chromatographic Determination of Permeability-Relevant Lipophilicity Facilitates Rapid Analysis of Macrocyclic Peptide Scaffolds.

Hydrocarbon-determined shake-flask measurements have demonstrated great utility for optimizing lipophilicity during early drug discovery. Alternatively, chromatographic methods confer reduced experimental error and improved handling of complex mixtures. In this study, we developed a chromatographic approach for estimating hydrocarbon-water shake-flask partition coefficients for a variety of macrocyclic peptides and other bRo5 molecules including PROTACs. The model accurately predicts experimental shake-flask measurements with high reproducibility across a wide range of lipophilicities. The chromatographic retention times revealed subtle conformational effects and correlated with the ability to sequester hydrogen bond donors in low dielectric media. Estimations of shake-flask lipophilicity from our model also accurately predicted trends in MDCK passive cell permeability for a variety of thioether-cyclized decapeptides. This method provides a convenient, high-throughput approach for measuring lipophilic permeability efficiency and predicting passive cell permeability in bRo5 compounds that is suitable for multiplexing pure compounds or investigating the properties of complex library mixtures.

A 2.4 GHz High-Efficiency Rectifier Circuit for Ambient Low Electromagnetic Power Harvesting

A novel 2.4 GHz high-efficiency rectifier circuit suitable for working under very-low-input electromagnetic (EM) power conditions (−20 to −10 dBm) is proposed for typical indoor power harvesting. The circuit features a SMS7630 Schottky diode in a series with a voltage booster circuit at the front end and a direct-current (DC)-pass filter at the back end. The voltage booster circuit consists of an asymmetric coupled transmission line (CTL) and a high-impedance microstrip line (of 100 Ω instead of 50 Ω) to significantly increase the potential at the diode’s input, thereby enabling the diode to operate effectively even in very-low-power environments. The experimental measurements show that the microwave direct-current (MW-DC) conversion efficiency of the rectifier circuit reaches 31.1% at a −20 dBm input power and 62.4% at a −10 dBm input power, representing a 7.4% improvement compared to that of the state of the art. Furthermore, the rectifier circuit successfully shifts the input power level corresponding to the peak rectification efficiency from 0 dBm down to −10 dBm. This design is a promising candidate for powering low-energy wireless sensors in typical indoor environments (e.g., the home or office) with low EM energy density.

Infrared emissivity measurement methods considering target reflective characteristics

Emissivity measurements are of great significance for infrared thermal radiation and infrared remote sensing. However, traditional methods often face challenges such as difficulties in non-contact measurement, small measurement areas, and unsuitability for non-Lambertian surfaces. To address these issues, we propose the reflective distribution model integral method (RDMIM). This method is based on a reflective distribution model using the scattering-reflective deviation angle (SRDA). By regressing and integrating the object’s reflective distribution model, it achieves accurate non-contact measurement of non-Lambertian surfaces under normal temperature conditions. Additionally, the measurement scheme has been further optimized to improve measurement efficiency while ensuring the accuracy of the model regression. Finally, the proposed RDMIM method has been validated through experimental measurements. The results have shown that this method has advantages in non-contact and large-area measurements. Moreover, the systematic error is smaller when the reflective characteristics of the reference body and the target are relatively similar.

Keep Your TEMPO Up: Nitroxide Radicals as Sensors of Intermolecular Interactions

This study examines experimental data on the influence of the surrounding medium and non-covalent interactions on the isotropic hyperfine coupling constant, Aiso(14N), of the stable nitroxide radical 2,2,6,6-Tetramethylpiperidin-1-yl)oxyl (TEMPO) in solution. The data were used to identify a density functional theory functional/basis set combination that accurately reproduces the experimental Aiso(14N) values. The variations in Aiso(14N) due to external factors are two orders of magnitude greater than the accuracy of its experimental measurements, making Aiso(14N) a highly sensitive experimental probe for quantifying these effects. Additionally, it was found that the proton-accepting ability of the N-O• moiety in TEMPO resembles that of the P=O moiety, enabling the simultaneous formation of two equally strong hydrogen bonds.

Predicting ignitability classification of thermally thick solids using hybrid GA-BPNN and PSO-BPNN algorithms

Two hybrid binary classification models combining the merits of GA (genetic algorithm) and PSO (particle swarm optimization) with BPNN (backpropagation neural network) are proposed to estimate the ignitability of thermally thick solids. A 1D numerical model verified by analytical correlation and experimental measurements is employed to yield training, validation and testing datasets. Surface absorptivity (ε), density (ρ), specific heat (Cp), thermal conductivity (k), critical temperature (Tcri), and incident heat flux (HF) which are highly related to solid ignition are selected as inputs, and ignitability serves as output. A one-hidden-layer BPNN is first constructed, and then GA and PSO are encoded to form GA-BPNN and PSO-BPNN where connection weights and biases are optimized by GA/PSO and the predicted error serves as objective function of GA/PSO. Results show that the optimal neuron number in hidden layer is 34. Compared to BPNN, GA-BPNN and PSO-BPNN feature higher accuracy but more training time. Accuracy and convergence efficiency of PSO-BPNN are higher than GA-BPNN, nevertheless GA-BPNN exhibits better robustness. Predictive accuracies of BPNN, GA-BPNN, and PSO-BPNN on testing dataset are 99.05%, 99.55%, and 99.55%, respectively. ROC (receiver operating characteristic) curves are plotted to rank the performance of the three models, revealing 0.5 is an appropriate threshold to classify ignitability. Feature importance of the models is analyzed using PI (permutation importance) and MIV (mean impact value) methods. It is found HF and Tcri are the most important two inputs followed by ε, while ρ, Cp, and k show least and approximately identical impact. Reliability of the three models is verified by predicting critical heat flux of ignition of three polymers, and good agreement with literature is observed.

Simulation of dissolution of cerium trifluoride in a mixture of LIF–NaF–KF

The study of phase diagrams of multicomponent molten mixtures is traditionally carried out either by experimental measurements or thermodynamic calculations based on known experimental data. Atomistic modeling occupies a significantly smaller share in the methodology, and the capabilities of this approach have been poorly studied. In this work, we simulated the dissolution of cerium trifluoride in the ternary eutectic of lithium, sodium, and potassium fluorides using the molecular dynamics method. A time- and ensemble-scale simulation of the coexisting crystalline phase and melt at several temperatures was carried out. The influence of ensemble size was studied. The rate of dissolution was studied depending on temperature. The asymptote of the dependence agrees well with the experimental liquidus temperature for a given composition. A conclusion is given about the possibility of using molecular dynamics to determine the complete solubility of a melt component.

Flow Field Analysis within Industrial Quenching Tanks Operated with Aqueous Polymer Solutions in the Steel Industry

Abstract Quenching with polymer solutions for steel heat treatment offers adjustable performance and improved ecological impact compared to conventional water or oil quenching. The submersion cooling operation poses challenges due to the intense vapor/polymer film collapse on the component surface, potentially destabilizing batch components. Therefore, controlled recirculation is critical to preventing tank and specimen damage during cooling and ensuring effective steel hardening. However, flow structure analysis is often overlooked and disregarded in industrial tank design. Thus, this study evaluates the tank geometries of two partner industries to determine if they provide the necessary homogeneous flow for optimal quenching. The analysis combines experimental velocity measurements with a numerical model, with the identification of different flow intensity regions being the main outcome of this work. The results suggest that geometric modifications could improve flow recirculation, enhancing quenching performance.

Experimental Characterization of the Isomer-Selective Generation of the Astrochemically Relevant Hydroxymethylene Radical Cation (HCOH•+/DCOH•+).

Interest in the observation and characterization of organic isomers in astronomical environments has grown rapidly with an increase in the sensitivity of detection techniques. Accurate modeling and interpretation of these environments require experimental isomer-specific reactivity and spectroscopic measurements. Given the abundance of formaldehyde (H2CO) in various astrophysical objects, the properties and reactivities of its cation isomers H2CO•+ and HCOH•+ are of significant interest. However, for the hydroxymethylene radical cation HCOH•+ (and its isotopologue DCOH•+), detailed reactivity studies have been limited by the lack of suitable experimental methods to generate this isomer with high purity. Here, potential approaches to the isomer-selective generation of HCOH•+ and DCOH•+ are characterized through differential reactivity measurements. While the dissociative photoionization of cyclopropanol (c-CH2CH2CHOH) is determined to be unsuitable, the dissociation of methanol-d3 (CD3OH) allows for the formation of DCOH•+ with a fractional abundance of >99% at photon energies below 14.8 eV. These results will allow future spectroscopic and reactivity measurements of HCOH•+/DCOH•+ to be conducted, laying the groundwork for future detection and incorporation into models of the interstellar medium.

Methods for separating the noise produced by the wheels and track during a train pass-by

AbstractRolling noise is produced by vibration of the wheels and track, induced by their combined surface roughness. It is important to know the relative contributions of the different sources, as this affects noise control strategies as well as acceptance testing of new rolling stock. Three different techniques are described that aim to use pass-by measurements to separate the wheel and track components of rolling noise. One is based on the TWINS model, which is tuned to measured track vibration. The second is based on the advanced transfer path analysis method, which provides an entirely experimental assessment. The third is based on the pass-by analysis method in combination with static vibroacoustic transfer functions which are obtained using a reciprocity method. The development of these methods is described and comparisons between them are presented using the results from three experimental measurement campaigns. These covered a metro train, a regional train and a high-speed train at a range of speeds. The various methods agree reasonably well in terms of overall trends, with moderate agreement in the mid-frequency region, and less consistent results at low and high frequency.

Data-driven discovery of chemotactic migration of bacteria via coordinate-invariant machine learning

BackgroundE. coli chemotactic motion in the presence of a chemonutrient field can be studied using wet laboratory experiments or macroscale-level partial differential equations (PDEs) (among others). Bridging experimental measurements and chemotactic Partial Differential Equations requires knowledge of the evolution of all underlying fields, initial and boundary conditions, and often necessitates strong assumptions. In this work, we propose machine learning approaches, along with ideas from the Whitney and Takens embedding theorems, to circumvent these challenges.ResultsMachine learning approaches for identifying underlying PDEs were (a) validated through the use of simulation data from established continuum models and (b) used to infer chemotactic PDEs from experimental data. Such data-driven models were surrogates either for the entire chemotactic PDE right-hand-side (black box models), or, in a more targeted fashion, just for the chemotactic term (gray box models). Furthermore, it was demonstrated that a short history of bacterial density may compensate for the missing measurements of the field of chemonutrient concentration. In fact, given reasonable conditions, such a short history of bacterial density measurements could even be used to infer chemonutrient concentration.ConclusionData-driven PDEs are an important modeling tool when studying Chemotaxis at the macroscale, as they can learn bacterial motility from various data sources, fidelities (here, computational models, experiments) or coordinate systems. The resulting data-driven PDEs can then be simulated to reproduce/predict computational or experimental bacterial density profile data independent of the coordinate system, approximate meaningful parameters or functional terms, and even possibly estimate the underlying (unmeasured) chemonutrient field evolution.

Solid-to-gas phase transition kinetics of diverse potassium occurrence forms during biomass pellet combustion: Time-resolved detection and multi-step modeling

The solid-to-gas phase transition of potassium during biomass combustion significantly impacts ash-related issues in bioenergy systems, affecting operational efficiency and equipment longevity. However, the specific mechanisms and kinetics of this transition process remain inadequately understood. This work investigates the time-resolved transition of solid-phase potassium to the gas phase during the combustion of rice husk and wheat straw pellets, combining experimental measurements with theoretical modeling. Tunable diode laser absorption spectroscopy (TDLAS) was employed to measure atomic potassium concentrations 15 mm above burning pellets tray, where gas-phase equilibrium is approached. Key combustion characteristics including thermogravimetric profiles, spectral radiation, and temperature were simultaneously monitored. A novel multi-step model was developed to describe the transition of different forms of solid-phase potassium (organic, exchangeable, and inorganic) to the gas phase. This model integrates TDLAS measurements, observed combustion characteristics, and biomass physicochemical properties. Thermodynamic equilibrium calculations were used to estimate the atomic potassium fraction from total gaseous potassium. The results showed that the solid-to-gas phase transition of organic potassium synchronizes with volatiles release. In contrast, the maximum emission rates of inorganic and exchangeable potassium occurred at the onset of char combustion. The developed model agrees well with the online detection experiments and were further validated by offline ICP analysis of residual ash. While not directly simulating gas-solid interface reactions near the particle surface, this work lays groundwork for future multi-scale modeling of particle-laden flows and reactor-scale phenomena in biomass combustion systems.

Experimental measurement and modeling of the internal pressure in cylindrical lithium-ion battery cells under abuse conditions

The internal pressure evolution of cylindrical lithium-ion battery cells under abuse tests is evaluated in this work. The pressure evolution is recorded through a cavity at the center of the inner structure of the cylindrical cell. Understanding pressure buildup due to exothermic reactions aids in designing safety features, such as improved venting mechanisms. The pressure activation for the safety mechanism depends on both the venting cap design and the different abuse test conditions. This study focuses on two important parameters: the state of charge (SOC) and the overheating ramp. The SOC significantly influences the venting pressure activation at the overheating abuse tests. The 18650 cell exhibits a similar venting behavior as the 21700 cell, with pressure safety mechanisms activating sooner for the 18650 cell. The geometric structure of the battery and safety mechanisms significantly influence vent-activation thresholds. After the Current Interruptor Device (CID) breakdown, pressure rises exponentially until venting occurs, due to gas generation from the decomposition of internal components and electrolyte vaporization. With the experimental data for the overheating abuse test, a model that predicts the evolution of the pressure has been developed. For the overcharging abuse test, the pressure evolution has been measured with different current rates (C-rates) to simulate overcharge with standard charging current and fast charging current. However, the internal pressure inside the cell activates the current interrupt device, which avoids overcharging the cell, thus preventing the thermal runaway (TR) from occurring.

Bubble plume effects on the flotation kinetics of nonmetallic inclusions based on experimental observations

AbstractSteelmaking has shown an increasing concern toward nonmetallic inclusions, leading to new technologies in the secondary metallurgy of steel. Although the typical inclusion removal procedure is by injecting inert gas into the ladle, this vessel does not fulfill all the requirements to accept a porous structure tailored to produce “clean steels.” Consequently, the spotlight has moved to the tundish, the last vessel before solidification, in which gas injection can continuously operate. Therefore, this work focuses on understanding the influence of typical gas flow rates (10–60 NL/min) on the kinetics of inclusion flotation, considering two bubble diameters (0.6 and 1.1 mm). For this purpose, experimental measurements were conducted in a water model, where glass hollow spheres played the role of inclusions, and their concentration was fitted by an exponential decay. In general, injecting bubbles into the system contributed positively to a faster and greater flotation of particles. The smaller bubbles led to a higher maximum efficiency, whereas the larger ones allowed a shorter time scale (i.e., a faster removal), defining a trade‐off to tune the bubble size. Regarding the gas flow rate, the results indicate an optimum range to decrease the time scale, and suggestions for bubble curtains in tundishes are drawn.

A Comparative Study on the Prediction of Hydrogen, Nitrogen, and Water Vapor of Regenerative Hydrogen Pump With Different Turbulence Models

Abstract The flow in a regenerative hydrogen pump used in a proton exchange membrane (PEM) fuel cell system is complex, including velocity mixing, streamline distortion, swirling flow, corner flow, and possible boundary separation. Experimental measurement and computational fluid dynamics (CFD) prediction are the main methods used to evaluate such performance. In this work, regenerative hydrogen designed for fuel cell electric vehicles with an impeller diameter of 91 mm and a shaft speed of 22,000 rpm is tested with a mixture of nitrogen, hydrogen, and water vapor. During the test, the flowrate and shaft speed ranges are 151–881 L/min and 8000–22,000 rpm, respectively. The inlet pressure and temperature are 171 kPa and 66 °C, respectively. Then, the flow is predicted by the k − ε model, the renormalization group (RNG) k − ε model, the explicit algebraic Reynolds stress models (EARSM) and the shear-stress transport (SST) detached eddy simulation (DES) model. The differences between the tested and predicted performances are compared. With these results, the velocity distribution in the side channel, the radial force of the impeller and casting, and the changes in the downstream pressure and velocity are analyzed in consideration of the ability of the turbulence models to simulate each characteristic flow. On this basis, the differences and abilities of the turbulence models for predicting the flow in regenerative hydrogen pumps are summarized.

A Multi-Scale Model for Predicting Physically Short Crack and Long Crack Behavior in Metals

The fatigue behavior of metal specimens is influenced by defects, material properties, and loading. This study aims to establish a multi-scale fatigue crack growth model that describes physically short crack (PSC) and long crack (LC) behavior. The model allows the calculation of crack growth rates for uniaxial loading at different stress ratios based on the material properties and specimen geometry. Furthermore, the model integrates the Gaussian distribution theory to consider material heterogeneity and the experimental measurement errors that cause fatigue scatter. The crack growth rate and fatigue life of metal specimens with different notch geometry were predicted. The curves generated by the multi-scale model were mainly consistent with the test data from the published literature at the PSC and LC stages.

A novel ventilation method: Experimental measurement and numerical simulation of vertical single-row inclined slit ventilation

Existing ventilation technologies often struggle to balance the limited airflow supply with the demands of indoor ventilation and air circulation in winter. This paper introduces a novel ventilation method, vertical single-row inclined slit ventilation (VSISV), which channels airflow through multiple inclined slits spaced at specific intervals along a vertical supply air duct. By ensuring continuity of the upstream and downstream slit jets, a continuous downward airflow is formed, enhancing both winter ventilation performance and indoor air circulation. The method was experimentally measured, with a focus on comparison to the IJV system. At the same airflow rate, it not only efficiently transports the supply air heat from the upper indoor area to the floor but also exhibits strong horizontal diffusion capability. The RNG turbulence model was selected for numerical simulation, focusing on indoor thermal comfort, air quality, and energy-saving potential. The results indicate that, at the same airflow rate, this method effectively maintains high indoor thermal comfort, directs airflow downward from the ceiling, reduces thermal stratification in the upper zone, achieves a more uniform and elevated indoor temperature, enhances indoor air quality, and demonstrates substantial energy-saving potential. This provides valuable insights for energy conservation, emission reduction, and the optimization in winter building ventilation systems.

Experimental Performance Analysis of Fabricated Si/Ge Thin Film Structure

This paper is devoted to the evaluation of a Silicon/Germanium (Si/Ge) thin film structure based on experimental measurements. An electron beam evaporator was used to fabricatethis structure. The sample was prepared under high vacuum conditions (pressure of 10-5 Torr, power of 6 kV and current of 200 mA). At these conditions, it was possible to get films with thickness of approximately 300 Å. The capacitance–voltage (C–V) and current–voltage (I–V) measurements of the sample were performed by a staircase sweep of voltages from 0 to 5 V and back from 5 to 0 V at room temperature. The sample exhibits a low hysteresis in measurements; this hysteresis is gradually removed when the sample is exposed to temperatures until 80 °C using a Carbolite Oven. It is also observed that both C-V and I-V characteristic curves of the sample has been smoothened. This sample exhibits an electroforming behavior as a metal-oxide-semiconductor (MOS) device over a short time duration of the selected staircase double sweep, hence it can be exploited as a fast switching element in digital microelectronic circuits. In addition, the hysteresis changes over the range from room temperature until 80 °C have opened the door to the possibility of exploiting this sample as a proximity temperature sensor within that range of temperature.

An In vivo Pilot Study to Estimate the Swelling of the Aneurysm Wall Rabbit Model Generated with Pulsed Fluid Against the Aneurysm Wall.

This study addresses the critical issue of evaluating the risk of rupture of unruptured intracranial aneurysms (UIAs) through the assessment of the mechanical properties of the aneurysm wall. To achieve this, an original approach based on the development of an in vivo deformation device prototype (DDP) of the vascular wall is proposed. The DDP operates by pulsing a physiological fluid onto the vascular wall and measuring the resulting deformation using spectral photon counting computed tomography (SPCCT) imaging. In this preliminary study conducted on a rabbit animal model, an aneurysm was induced on the carotid artery, followed by deformation of the aneurysm sac wall using the DDP. The change in luminal volume of the aneurysm sac induced by the deformation of the vascular wall was then quantified. The initial experimental results demonstrated an increase in the luminal volume of the aneurysm sac in relation to the increased flow rate of the fluid pulsed by the DDP onto the arterial wall. Measurement of the pressure generated by the DDP in relation to the different flow rate values imposed by the pulsation system revealed experimental values of the same order of magnitude as dynamic blood pressure. Furthermore, theoretical pressure values on the deformed area, calculated using Euler's theorem, appeared to be correlated with experimental pressure measurements. This equivalence between theory and experiment is a key element in the use of the DDP for estimating the mechanical properties of the vascular wall, particularly for the use of finite element models to characterise the stress state of the deformed vascular wall. This preliminary work thus presents a novel, innovative, and promising approach for the evaluation and management of the risk of rupture of unruptured intracranial aneurysms.

A wall-modeled large-eddy simulation of the unsteady flow in a water-jet pump based on the turbulent length scales

Non-zonal hybrid Reynolds-averaged Navier-Stokes (RANS) and large-eddy simulation (LES) are commonly used for predicting unsteady flows in pumps. However, these hybrid methods often face challenges in the transition between the RANS and LES regions, known as the “grey area” problem. In this study, a pure LES approach, coupled with wall modeling to eliminate this issue, was employed to simulate unsteady flow in a water-jet pump (WJP). A grid generation method, based on turbulent length scales derived from a precursor RANS calculation, was developed to address the complexities of grid design in wall-modeled large-eddy simulations (WMLESs) for complex geometries. Initially, the accuracy of the wall model was validated in the fully developed channel flow. The grid generation method and the accuracy of WMLES in predicting unsteady fluctuations were validated using the flow over a flat strut with an asymmetrically beveled trailing edge. The predicted fluctuation spectra at monitored points showed strong agreement with experimental data. The grid generation method was then applied to WMLES of flow in a WJP, where experimental measurements of the pump head rise and torque were accurately calculated by the numerical simulations. The results from three different grid resolutions were compared with experimental data, revealing significant changes in the predicted mean and transient fields as the grid was refined. The grid based on the turbulent length scales reproduced both the hydrodynamics and transient vortices with high accuracy and detailed resolution. These comparisons demonstrated that pure LES with wall modeling effectively predicts the mean and fluctuating characteristics of high Reynolds number flows with reasonable computational cost. The turbulent length scale-based WMLES enhance the fidelity of computational tools for simulating pump flows.

Noise aware parameter estimation in bioprocesses: Using neural network surrogate models with nonuniform data sampling

AbstractThis article demonstrates a parameter estimation technique for bioprocesses that utilizes measurement noise in experimental data to determine credible intervals on parameter estimates, with this information of potential use in prediction, robust control, and optimization. To determine these estimates, the work implements Bayesian inference using nested sampling, presenting an approach to develop neural network‐ (NN) based surrogate models. To address challenges associated with nonuniform sampling of experimental measurements, an NN structure is proposed. The resultant surrogate model is utilized within a Nested Sampling Algorithm that samples possible parameter values from the parameter space and uses the NN to calculate model output for use in the likelihood function based on the joint probability distribution of the noise of output variables. This method is illustrated against simulated data, then with experimental data from a Sartorius fed‐batch bioprocess. Results demonstrate the feasibility of the proposed technique to enable rapid parameter estimation for bioprocesses.