Tuning Effect Research Articles

Effective tuning of spin mixing conductance at the Py/Cu–Nd interface

Spin mixing conductance (SMC) at the ferromagnetic/non-magnetic material (FM/NM) interface governs the transport efficiency of the spin current. A high level of SMC is crucial for efficient spin injection and spin manipulation. Here, we report a reliable way to enhance the SMC at the FM/NM interface by rare-earth doping in the NM layer. As evidenced by the decreased saturation magnetization in permalloy (Py)/Cu–Nd structures, an induced magnetism in Nd is proposed, which is likely to be antiferromagnetically coupled to Py at the interface. By changing the doping content of Nd, the Py/Cu–Nd interface can be well designed, which gives rise to an effective tuning of the SMC from 0.37 × 1015 to 16.26 × 1015 cm−2. Such a tuning effect of SMC is suppressed by inserting a Cu spacer, demonstrating the key role of the antiferromagnetically coupled interface to the improved SMC. Our results highlight the significance of rare-earth materials in spin transport, expanding the design capability of energy-efficient spintronic devices.

“Stacked-arene”-type organocatalysts: Utilization of π-π interaction as an electron tuning tool

We have developed a novel type of organocatalyst having a 1,8‑diarylnaphthalene core, namely, a “Stacked-arene”-type organocatalyst. The key point of the catalyst design is the π-π interaction based electron tuning. This through space electron tuning effect is effective enough for the improvement of the catalytic activity (reducing the catalyst loading), and this tendency was evaluated by the Michael addition reaction between nitrostyrene and dimethyl malonate. Additional experiments suggest that the expected improvement of the hydrogen bonding ability plays a critical role in the high performance of the designed catalyst.

Chaotic dynamics induced by anti-slug control in offshore oil production plants

The dynamic behavior of an offshore oil production plant obtained through an anti-slug scheme based on a feedback PI control was studied. Three different SISO control scenarios were evaluated considering different controlled variables and the effect of the PI control constants tuning on the suppression of oscillatory behavior related to the slugging flow phenomenon. It was shown that the PI control, when the pressure at the top of the riser was the controlled variable, was able to induce chaotic oscillations. Additionally, the proper characterization of the chaotic behavior was performed allowing to determine the PI control tuning conditions that favor the appearance of this complex behavior.

Effect of Al composition tuning on properties and structure in Zr-Co-Al metallic glass

The variation of glass forming ability, mechanical properties and atomic structure in Zr72−xCo28Alx (x = 14–19 at%) and Zr56Co44−xAlx (x = 13–20 at%) bulk metallic glasses derived from composition tuning has been studied by using copper-mold suction, high-energy X-ray diffraction, extended X-ray absorption fine structure experiments and ab initio molecular dynamics simulations, respectively. Both experimental and theoretical results indicate that glass forming ability was enhanced when composition approached Zr56Co28Al16, attributed to the densification in the first shell around Co atoms with the increase of 11-coordinated polyhedra. In addition, a ductile-to-brittleness transition induced by minor substitution of larger-sized atoms by smaller-sized atoms has been found. As good glass forming ability is usually accompanied by dense atomic arrangement, the increasing larger-sized atoms may tend to offer more free volume which is beneficial to plastic flow. Compositional tuning, along with structural tuning to influence both glass forming ability and plastic flow, offers a reliable method to develop ductile metallic glasses.

Geostatistical Inversion of Spectrally Broadened Seismic Data for Re-Evaluation of Oil Reservoir Continuity in Inas Field, Offshore Malay Basin

The application of geostatistics in seismic inversion techniques has been proven somewhat reliable in the delineation of reservoir properties and has recently attracted the attention of many geoscientists. However, there are cases where its prediction returned negative results after drilling. In this research, we re-evaluated a reservoir in Inas Field, whose geostatistical inversion result wrongly predicted sand continuity, resulting in the spudding of a dry hole. When a geostatistical seismic inversion is successfully applied, it provides an increase in seismic resolution and aids the prediction of sand continuity. Although this method relies more on the statistical data from a well because of the limitation of the seismic data in resolving thin geologic features, the spatial variation of reservoir parameters still depends on seismic data, which often have poor resolution quality. Therefore, to investigate the impact of bandlimited data on the geostatistical inversion, we harmonically extended the seismic bandwidth by applying a sparse-layer spectral inversion algorithm to the data. This algorithm increased the seismic data bandwidth from 80 Hz to 180 Hz, and its tuning thickness reduced from 32 m to 10 m at the reservoir interval. The resultant broadband (180 Hz), as well as the original seismic (narrowband of 80 Hz) data, were both used as input to build two separate geostatistical prediction models, respectively. Twenty (20) realizations of these models were generated, ranked into P10, P50, and P90, and the best case was selected for interpretation. These realizations were used to characterize the reservoir lithofacies distribution. When compared, the result of the broadband inversion, facies and sand distribution model showed that the reservoir facies changed towards the location of the dry well. The broadband geostatistical inversion efficiently improved the reservoir characterization process by not only producing an accurate estimation of the lateral extent of the reservoir heterogeneities but also generating outcomes that help us understand why other geostatistical inversion analyses of the target reservoir were misleading. Contrary to the popular assumption, it was discovered that the tuning effects of bandlimited data could affect the result of a geostatistical inversion and result in wrong facies predictions.

Accelerating CO2 reduction on novel double perovskite oxide with sulfur, carbon incorporation: Synergistic electronic and chemical engineering

Perovskite semiconductor materials attracted tremendous interest in heterogeneous photocatalysis. However, most of these semiconductors have limited charge mobility and poor charge separation. Using a flux-assisted technique, we synthesized high symmetry anisotropic facets (18-facet Sr2CoTaO6) double perovskite oxide semiconductor. Surface doping of sulfur (S) and carbon (C) into the lattice of a particulate novel Sr2CoTaO6 induced microstrain to enhance the photocatalytic conversion of CO2 by boosting charge density to tune charge-carrier mobility. The S and C incorporation boosted the photocatalytic CO2 reduction more than eleven orders of magnitude higher than pristine Sr2CoTaO6 under visible light irradiation. Such efficient photocatalytic CO2 reduction is attributed to the synergistic effect of tuning the carriers mobility and spatial charge separation via chemical and electronic engineering of the particulate (S, C)-codoped Sr2CoTaO6. The concept of fabrication of spatial charge separation and engineering electron mobility will explore a new avenue to design an efficient photocatalytic system for the conversion of solar energy to solar fuels.

Data-Enabled Predictive Control for Building HVAC Systems

AbstractModel predictive control is widely used as a control technology for the computation of optimal control inputs of building heating, ventilating, and air conditioning (HVAC) systems. However, both the benefits and widespread adoption of model predictive control (MPC) are hindered by the effort of model creation, calibration, and accuracy of the predictions. In this paper, we apply the data-enabled predictive control (DeePC) algorithm for designing controls for building HVAC systems. The algorithm solely depends on input/output data from the system to predict future state trajectories without the need for system identification. The algorithm relies on the idea that a vector space of all input–output trajectories of a discrete-time linear time-invariant (LTI) system is spanned by time-shifts of a single measured trajectory, given the input signal is persistently exciting. Closed-loop simulations using EnergyPlus are performed to demonstrate the approach. The simulated building modeled in EnergyPlus is a modified commercial large office prototype building served by an air handling unit-variable air volume HVAC system. Temperature setpoints of zones are used as control variables to minimize the HVAC energy cost of the building considering a time-of-use electricity rate structure. Furthermore, sensitivity analysis is conducted to gain insights into the effect of parameter tuning on DeePC performance. Simulation results are used to illustrate the performance of the algorithm and compare the algorithm with model-based MPC and occupancy-based setpoint controller. Overall, DeePC achieves similar performance compared to MPC for lower engineering effort.

Tuning the Thermoelectric Properties of Boron‐Doped Silicon Nanowires Integrated into a Micro‐Harvester

AbstractSemiconductor nanowires demonstrate outstanding properties within a wide range of application fields, including energy harvesting. Consequently, increasing attention has been focused on silicon nanowires for thermoelectric power generation after their successful implementation in miniaturized devices. In this work, the effect of tuning the p‐type doping level in silicon nanowires is studied in thermoelectric micro‐generators. In situ heavily boron‐doped silicon nanowires are grown by the vapor‐liquid‐solid mechanism in a chemical vapor deposition reactor. Variable quantities of dopant gas—diborane or B2H6—are used to control the doping level and study the direct influence on the thermoelectric properties of the nanowires. The relationship between dopant partial pressure and resulting carrier concentration is evaluated, and all thermoelectric properties are studied as a function of each doping level. A maximum power factor of 2.0 ± 0.2 mW mK−2 is found for a carrier concentration of 2.5 × 1019 cm−3, corresponding to a figure of merit of zT = 0.04 at near room temperature. Optimized nanowires are included in a micro‐machined thermoelectric generator, yielding an open circuit voltage of 4.5 mV and maximum power output of 1.08 μW cm−2 upon hot surfaces at 250 °C.

Origin and strain tuning of charge density wave in LaTe3

We study the origin of charge density wave (CDW) in rare-earth tritelluride LaTe3 and the strain tuning effect on CDW in monolayer LaTe3 by first-principles calculations. The calculations of the electronic structure, phonon spectrum, electron susceptibility, and electron-phonon coupling (EPC) matrix show that the origin of CDW in LaTe3 is momentum-dependent EPC rather than Fermi-surface nesting. The unidirectional CDW with a wave-vector QCDW≈2/7c∗ is the most stable state. We study the biaxial strain effect on CDW in monolayer LaTe3 by evaluating the total energy, CDW-related lattice distortions, and phonon spectra. Our results show that the tensile strain can enhance CDW order, while the compressive strain can inhibit CDW order. As the CDW order is fully suppressed, superconductivity could be induced. Our study may help to clarify the mechanism of CDW in LaTe3 and find the effective tuning method of CDW in monolayer or few-layer configuration.

Pressure-Induced Superconductivity in HgTe Single-Crystal Film.

HgTe film is widely used for quantum Hall well studies and devices, as it has unique properties, like band gap inversion, carrier‐type switch, and topological evolution depending on the film thickness modulation near the so‐called critical thickness (63.5 Å), while its counterpart bulk materials do not hold these nontrivial properties at ambient pressure. Here, much richer transport properties emerging in bulk HgTe crystal through pressure‐tuning are reported. Not only the above‐mentioned abnormal properties can be realized in a 400 nm thick bulk HgTe single crystal, but superconductivity is also discovered in a series of high‐pressure phases. Combining crystal structure, electrical transport, and Hall coefficient measurements, a p‐n carrier type switching is observed in the first high‐pressure cinnabar phase. Superconductivity emerges after the semiconductor‐to‐metal transition at 3.9 GPa and persists up to 54 GPa, crossing four high‐pressure phases with an increased upper critical field. Density functional theory calculations confirm that a surface‐dominated topologic band structure contributes these exotic properties under high pressure. This discovery presents broad and efficient tuning effects by pressure on the lattice structure and electronic modulations compared to the thickness‐dependent critical properties in 2D and 3D topologic insulators and semimetals.

The effect of pressure tuning on the structure and mechanical properties of high-entropy carbides

High-purity (Ta0.2Nb0.2Hf0.2Zr0.2Ti0.2)C high-entropy ceramics are successfully synthesized using high-pressure solid-phase reactions. The high-entropy phase formation route and the effect of pressure tuning on the structural and mechanical properties of the ceramics are systematically investigated. An interesting process is observed whereby a double phase high-entropy carbide is formed, followed by the defusing of one phase into another, and the eventual formation of a high-entropy phase. Moreover, an abnormal, pressure-induced enhancement of the bulk modulus and strength is observed, resulting from the crystal plane distortion in high-entropy carbides. These results reflect the unusual lattice distortion effects of continuous, pressure-induced tuning on the structure and properties of high-entropy carbides.

Bandgap tuning of pseudoboehmite nanoparticles induced by quantum confinement

In this work we report the bandgap tuning of pseudoboehmite achieved by high energy ball milling. The milling process of pseudoboehmite was performed using a SPEX 8000 mill for of up to 10 h. Pseudooehmite powders were characterized before and after milling by X-ray diffraction, Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy (XPS) and electron energy loss spectroscopy (EELS). XRD results confirmed that the pseudoboehmite structure is preserved for 10 h of milling. The crystallite size of pseudoboehmite was estimated by Scherer equation, observing a constant increase with milling time from 2.72 nm to 3.18 nm. The pseudobohemite has a reduction in d-spacing with milling in the “c-axis”, but an enlargement in the “a-b directions”. The “c-axis” modification is due to water release, while the “a-b-plane” enlargement is attributed to cold welding during milling. The XPS and EELS analyses demonstrated a bandgap tuning effect from 7.11 eV to 6.20 eV in the raw and milled pseudoboehmite for 10 h. The results were demonstrated analytically.

On the domain aided performance boosting technique for deep predictive networks: A COVID-19 scenario

Deep learning models are one of the widely used techniques for forecasting time series data in various applications. It has already been established that the Recurrent Neural Networks (RNN) such as the Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), etc., perform well in analyzing sequence data for accurate time-series predictions. But, these specialized recurrent architectures suffer from certain drawbacks due to their computational complexity and also their dependency on short term historical data. Hence, there is a scope for further improvement. This paper analyzes the effects of various optimizers and hyper-parameter tuning, on the precision and time efficiency of different deep neural architectures. The analysis has been conducted on COVID-19 pandemic data. Since Convolutional Neural Networks (CNN) are known for their super-human ability in identifying patterns from images, the time-series data has been transformed into a slope-information domain for analyzing the slope patterns over time. The domain patterns have been projected on a 2D plane for further analysis using a restricted recursive CNN (RRCNN) algorithm. The experimental results reveal that the proposed methodology reduces the error over benchmarked sequence models by almost 20% and further reduces the training time by nearly 50%. The prediction models considered in this study have been evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE%).

The Effects of the Structural Tuning on the Sensing Performance of Extinguishant Detector Based on the Differential Pressure Principle

The research of the spatial and temporal distribution of the extinguishant concentration is necessary for the design of a fire extinguishing system. The detection technique based on the differential pressure principle is very suitable for the extinguishant concentration measurement. This paper focused on studying the effect of sensing structure on sensing performance. It was found that due to the solid sensing structure, the sensor had good stability and reproducibility, and the response/sensitivity would increase with the increase of the throat diameter of the critical flow nozzle and temperature, and with the decrease of the inner diameter and the number of the tubes of the vascular bundles, which were in good agreement with the theoretical prediction. For the response rate, it could be improved by increasing the throat diameter and decreasing the external diameter of the vascular bundles, but these also caused the great increase in the response fluctuation, which could lead to the decrease in measuring accuracy despite the improvement in response and sensitivity, especially for the high concentration. However, the increase of the temperature would efficiently reduce the response fluctuation while improving the response and sensitivity. The research results can provide good theoretical suggestions for the sensor design.

A multiple alignment workflow shows the effect of repeat masking and parameter tuning on alignment in plants.

Alignments of multiple genomes are a cornerstone of comparative genomics, but generating these alignments remains technically challenging and often impractical. We developed the msa_pipeline workflow (https://bitbucket.org/bucklerlab/msa_pipeline) to allow practical and sensitive multiple alignment of diverged plant genomes and calculation of conservation scores with minimal user inputs. As high repeat content and genomic divergence are substantial challenges in plant genome alignment, we also explored the effect of different masking approaches and parameters of the LAST aligner using genome assemblies of 33 grass species. Compared with conventional masking with RepeatMasker, a masking approach based on k-mers (nucleotide sequences of k length) increased the alignment rate of coding sequence and noncoding functional regions by 25 and 14%, respectively. We further found that default alignment parameters generally perform well, but parameter tuning can increase the alignment rate for noncoding functional regions by over 52% compared with default LAST settings. Finally, by increasing alignment sensitivity from the default baseline, parameter tuning can increase the number of noncoding sites that can be scored for conservation by over 76%. Overall, tuning of masking and alignment parameters can generate optimized multiple alignments to drive biological discovery in plants.

An Empirical Study of the Impact of Hyperparameter Tuning and Model Optimization on the Performance Properties of Deep Neural Networks

Deep neural network (DNN) models typically have many hyperparameters that can be configured to achieve optimal performance on a particular dataset. Practitioners usually tune the hyperparameters of their DNN models by training a number of trial models with different configurations of the hyperparameters, to find the optimal hyperparameter configuration that maximizes the training accuracy or minimizes the training loss. As such hyperparameter tuning usually focuses on the model accuracy or the loss function, it is not clear and remains under-explored how the process impacts other performance properties of DNN models, such as inference latency and model size. On the other hand, standard DNN models are often large in size and computing-intensive, prohibiting them from being directly deployed in resource-bounded environments such as mobile devices and Internet of Things (IoT) devices. To tackle this problem, various model optimization techniques (e.g., pruning or quantization) are proposed to make DNN models smaller and less computing-intensive so that they are better suited for resource-bounded environments. However, it is neither clear how the model optimization techniques impact other performance properties of DNN models such as inference latency and battery consumption, nor how the model optimization techniques impact the effect of hyperparameter tuning (i.e., the compounding effect). Therefore, in this paper, we perform a comprehensive study on four representative and widely-adopted DNN models, i.e., CNN image classification , Resnet-50 , CNN text classification , and LSTM sentiment classification , to investigate how different DNN model hyperparameters affect the standard DNN models, as well as how the hyperparameter tuning combined with model optimization affect the optimized DNN models, in terms of various performance properties (e.g., inference latency or battery consumption). Our empirical results indicate that tuning specific hyperparameters has heterogeneous impact on the performance of DNN models across different models and different performance properties. In particular, although the top tuned DNN models usually have very similar accuracy, they may have significantly different performance in terms of other aspects (e.g., inference latency). We also observe that model optimization has a confounding effect on the impact of hyperparameters on DNN model performance. For example, two sets of hyperparameters may result in standard models with similar performance but their performance may become significantly different after they are optimized and deployed on the mobile device. Our findings highlight that practitioners can benefit from paying attention to a variety of performance properties and the confounding effect of model optimization when tuning and optimizing their DNN models.

Relationship between the VOC Tuning Effect and the Interface Activation Energy Due to the Third Component Concentration in Ternary Organic Solar Cells

The open-circuit voltage (VOC) tuning effect due to the variation of the low concentration of the third component in ternary organic solar cells has been mainly attributed to interfacial phenomena. Up to date, the models reported in the literature to analyze such interfacial phenomena are based on optical characterization. In this work is proposed a different approach to study such a VOC tuning effect by using dark-current characteristics at different temperatures. Specifically, for PTB7-Th:PC71BM:ICBA-based solar cells, it is found that an increment of the third component concentration, that is, the fullerene ICBA, causes an increase in the activation energies (Ea) in an Arrhenius-type curve. This, in turn, decreases the reverse saturation current (J0) of the devices, thereby incrementing their VOC.

Effect of Magnetic Tuning and Induced Charge Transfer Isotropy in a CrI3-Based 2D Trilayer Heterostructure

Effect of Magnetic Tuning and Induced Charge Transfer Isotropy in a CrI₃-Based 2D Trilayer Heterostructure

The Global Atmosphere-aerosol Model ICON-A-HAM2.3-Initial Model Evaluation and Effects of Radiation Balance Tuning on Aerosol Optical Thickness.

The Hamburg Aerosol Module version 2.3 (HAM2.3) from the ECHAM6.3‐HAM2.3 global atmosphere‐aerosol model is coupled to the recently developed icosahedral nonhydrostatic ICON‐A (icon‐aes‐1.3.00) global atmosphere model to yield the new ICON‐A‐HAM2.3 atmosphere‐aerosol model. The ICON‐A and ECHAM6.3 host models use different dynamical cores, parameterizations of vertical mixing due to sub‐grid scale turbulence, and parameter settings for radiation balance tuning. Here, we study the role of the different host models for simulated aerosol optical thickness (AOT) and evaluate impacts of using HAM2.3 and the ECHAM6‐HAM2.3 two‐moment cloud microphysics scheme on several meteorological variables. Sensitivity runs show that a positive AOT bias over the subtropical oceans is remedied in ICON‐A‐HAM2.3 because of a different default setting of a parameter in the moist convection parameterization of the host models. The global mean AOT is biased low compared to MODIS satellite instrument retrievals in ICON‐A‐HAM2.3 and ECHAM6.3‐HAM2.3, but the bias is larger in ICON‐A‐HAM2.3 because negative AOT biases over the Amazon, the African rain forest, and the northern Indian Ocean are no longer compensated by high biases over the sub‐tropical oceans. ICON‐A‐HAM2.3 shows a moderate improvement with respect to AOT observations at AERONET sites. A multivariable bias score combining biases of several meteorological variables into a single number is larger in ICON‐A‐HAM2.3 compared to standard ICON‐A and standard ECHAM6.3. In the tropics, this multivariable bias is of similar magnitude in ICON‐A‐HAM2.3 and in ECHAM6.3‐HAM2.3. In the extra‐tropics, a smaller multivariable bias is found for ICON‐A‐HAM2.3 than for ECHAM6.3‐HAM2.3.

A Machine Learning-Based Approach to Clinopyroxene Thermobarometry: Model Optimization and Distribution for Use in Earth Sciences.

Thermobarometry is a fundamental tool to quantitatively interrogate magma plumbing systems and broaden our appreciation of volcanic processes. Developments in random forest‐based machine learning lend themselves to a data‐driven approach to clinopyroxene thermobarometry, allowing users to access large experimental data sets that can be tailored to individual applications in Earth Sciences. We present a methodological assessment of random forest thermobarometry using the R freeware package extraTrees. We investigate the model performance, the effect of hyperparameter tuning, and assess different methods for calculating uncertainties. Deviating from the default hyperparameters used in the extraTrees package results in little difference in overall model performance (<0.2 kbar and <3°C difference in standard error estimate, SEE). However, accuracy is greatly affected by how the final value from the distribution of trees in the random forest is selected (mean, median, or mode). Using the mean value leads to higher residuals between experimental and predicted P and T, whereas using median values produces smaller residuals. Additionally, this work provides two scripts for users to apply the methodology to natural data sets. The first script permits modification and filtering of the model calibration data set. The second script contains premade models, where users can rapidly input their data to recover PT estimates (SEE clinopyroxene‐only model: 3.2 kbar, 72.5°C and liquid‐clinopyroxene model: 2.7 kbar, 44.9°C). Additionally, the scripts allow the user to estimate the uncertainty for each analysis, which in some cases is significantly smaller than the reported SEE. These scripts are open source and can be accessed at https://github.com/corinjorgenson/RandomForest-cpx-thermobarometer.