Orientation State Research Articles

Blue Phase-Polymer-Templated Ferroelectric Nematic Liquid Crystal.

Ferroelectric nematic liquid crystals (NFLCs) are promising electronic materials owing to their giant permittivity, polarization, and nonlinear optical coefficients. Although it is difficult to polymer-stabilize the NFLC orientation state due to their low affinity to polymers, in this study, we have succeeded in stabilizing the orientation into a blue phase (BP) three-dimensional structure by using BP polymer networks. This BP polymer-templated NFLC retains their ferroelectric properties and stabilizes with macroscopically canceled polarization due to the BP structure. The large polarization and dielectric constant of the NFLCs resulted in a large electro-optical Kerr effect. In addition, Curie point was observed in the BP polymer-templated NFLC, indicating the existence of multiple kinetic modes in the NFLCs themselves. This breakthrough expands the range of applications for both BPs and NFLCs.

Sex, sexual orientation, and gender identity data collection across electronic health record platforms: a national cross-sectional survey.

To assess the current state of sex, sexual orientation, and gender identity (SSOGI) data collection options in US electronic health record (EHR) platforms. We utilized an anonymous survey distributed via purposive snowball sampling to assess EHR platforms across the United States. Of 90 surveys started, 41 (45.6%) were completed and used for data analysis. Respondents represented a geographically diverse sample of health care centers across the United States. EPIC was the most used EHR platform (70.7%) followed by Cerner (9.8%). Across reported platforms, a majority utilized structured fields to collect and document patient SSOGI data (n = 25, 61.0%). There was variability across platforms regarding SSOGI data elements collected. No platform collected all recommended SSOGI data elements. Significant variation exists across EHR platforms and across health care settings using the same EHR platform. National standards need to be followed for SSOGI data collection in EHR platforms.

Efficient Approximation of Varying Fiber Orientation States in Injection Molded Parts Under Consideration of Multiple Manufacturing Uncertainties

AbstractThe production of high-quality fiber reinforced polymer parts is an important aspect in several industrial areas. However, due to unavoidable uncertainties in material and manufacturing processes, the part quality scatters. One important aspect here is the fiber orientation, being crucial for the thermo-mechanical properties of the part and being influenced by the uncertain material state and process conditions. Process simulations are an important tool for predicting the fiber orientation, but state-of-the-art simulations are normally deterministic and represent only one specific case. Performing enough deterministic simulations to model manufacturing uncertainties requires high numerical effort. Therefore, this work presents methods to quickly and efficiently approximate the fiber orientation under varying material and process parameters, requiring only a few simulations as input. Different schemes for approximation are evaluated and compared with each other and with 3D process simulations.

The use of digital thread for reconstruction of local fiber orientation in a compression molded pin bracket via deep learning

A deep convolutional neural network (DCNN) was used for microstructure reconstruction using artificial intelligence (MR-AI) by predicting local average fiber orientation distributions (FOD) in a 3D prepreg platelet molded composite (PPMC) pin bracket. To train the MR-AI model, surface strain fields from residual stresses simulated in PPMC plates were used as the input to the DCNN. A training dataset included PPMC plates with various degrees of global fiber alignment, based on the information obtained from high-fidelity flow simulation of a pin bracket. The MR-AI model was then deployed to analyze FOD in the 3D pin bracket by conducting thermo-elastic residual stress analysis. Initially, the MR-AI model was established entirely on the synthetic simulation data. Then, a μCT scan of a physically molded pin bracket was used to create a finite element model that provided data for additional validation of the DCNN model. For the μCT scan finite element pin bracket the MR-AI model predicted the distribution of fiber orientation tensor components with MAE of 0.10 indicating a global prediction error of 10 %. For the flow simulated pin bracket, the MR-AI model predicted the distribution of fiber orientation tensor components with a global prediction error of 11 %. The MR-AI model showed the ability to predict regions of varying alignment in the base and flange of the pin bracket. The proposed MR-AI methodology allows for rapid prediction of FOD in geometrically complex parts and offers a promising path to detecting unique fiber orientation states in molded components.

Motion planning of free-floating space robots for tracking tumbling targets by two-axis matching via reinforcement learning

The increasing amount of space debris poses a persistent threat to the safety of spacecraft. These non-cooperative targets are generally in a tumbling state, which places stringent requirements on the precision of the end-effector pose for capture tasks. Moreover, there are still difficulties in exploring the orientation state space, making it impractical to track targets with complex tumbling states based on reinforcement learning. To track tumbling targets quickly with high accuracy, we propose Two-Axis Matching Path Tracking (TMPT) algorithm. Our algorithm reduces the complexity of orientation exploration and improves the convergence speed through two-axis matching and the self-guidance module. Furthermore, the training environment has been redesigned by adopting staged tumbling target environment, which expands the range of trackable targets. Comparative simulation results demonstrate the efficiency and robustness of the algorithm.

The mechanism of tuning filler orientation degree in composites based on AC electric field assist: from microscopic dynamical model to macroscopic electrical properties

Using the AC electric field to induce the orientation of nonlinear conductive fillers in composites is an effective solution for alleviating electric field distortion in power modules. However, the mechanism by which the electric field affects the filler dynamic characteristics and the composites’ electrical properties remains unclear. In this paper, the correlation between the microscopic dynamic processes of fillers and the macroscopic current amplitude was analyzed. The results show that the current increases rapidly (0 ∼ 173 s) and then slowly (173 ∼ 869 s) at 600 V mm−1, influenced by the rotation and attraction processes of the fillers. This demonstrates that the orientation stops at about 869 s and the filler orientation state is a key factor in determining the dielectric properties. Secondly, the global orientation evaluation index D for the filler network was proposed, which can also derive the minimum time and energy loss required for preparation. Finally, the impact of different filler orientations on the composites’ conductivity was investigated. In the low electric field stress region, with the average carrier jump distance decreasing from 150.23 to 109.71 nm as the D increases from −0.93 to −0.05. On this basis, materials with nonlinear conductivity gradient distribution can be easily prepared. Before optimization, the electric field stress of the power module at the triple point was 35.79 kV. This composite can reduce the value to 15.42 kV, a decrease of 56.9%, while maintaining good electric field uniformity.

Anethole Regulated Crystallization for High Efficiency Carbon‐Based Perovskite Solar Cells

AbstractTwo‐step sequential deposition is a widespread technique for the fabrication of perovskite films, renowned for its better control of the crystallization process. However, achieving a well‐controlled and complete reaction of PbI2 by organic ammonium salts remains a key challenge. Previous studies have predominantly focused on regulating the properties of the PbI2 layer while paying less attention to the high reactivity of organic ammonium salts. In this study, the natural molecule anethole is first explored to control perovskite crystallization during two‐step sequential deposition, focusing on the reactivity modulation of organic formamidine ion (FA+). It is demonstrated that FA+ exhibits strong hydrogen bond interactions with anethole, inhibiting the high reactivity of FA+ and effectively delaying the rapid reaction between FAI and PbI2. This decelerates the crystallization kinetics of perovskite films, facilitating the orderly and complete reaction of PbI2 by FAI while suppressing detrimental δ‐phase formation. Consequently, FA‐based perovskite films with high crystallinity, preferred orientation, and low defect state density are obtained. The fabricated planar hole transport layer‐free carbon electrode perovskite solar cells deliver an efficiency of 20.41% (certified efficiency of 20.0%), which is a new record for this kind of solar cell.

Serendipitous relationship between discrete distribution models representing random orientations of fillers in composite materials and the golden ratio

The purpose of this study is to establish simple discrete distribution models capable of expressing the two and three-dimensional random orientation states of fillers in composite materials. Initially, a new method is proposed to divide the elastic constants into isotropic and anisotropic parts. Employing this method, for the composite material containing ellipsoidal fillers with various orientations in the material, the macroscopic elastic constants and thermal expansion coefficients of the material are derived based on the Mori–Tanaka method. Subsequently, the analysis of the macroscopic properties is performed for fillers oriented two and three-dimensional discrete distributions. As a result, it is found that when both angular pitches of the azimuth angle and the rotational angle of the fillers in a plane are at most 72°, the macroscopic elastic constants and the thermal expansion coefficients of the material exhibit in-plane isotropy, leading to the realization of a two-dimensional random state. Furthermore, if the angular distribution of the zenith angle and azimuth angle corresponds to the vertices of the regular dodecahedron and icosahedron, and the angular pitch of the rotational angle of the fillers is at most 72°, the macroscopic properties of the material become isotropic, resulting in a three-dimensional random state. Notably, the angle of 72° and the orientation angles of the vertices of the regular dodecahedron and icosahedron are associated with the golden ratio. Therefore, this analysis suggests a serendipitous relationship between the golden ratio and the angular pitch that simplifies the random orientation distribution of fillers.

Influence of Flow-Induced Polymorphism and Fiber Morphology on Mechanical Behavior in Long Discontinuous Glass Fiber Polyamide Composites

This study investigates the combined flow-induced crystallization in the polymer and fiber reorientation during compression molding of a long discontinuous glass fiber polyamide (PA 6) composite. The composite is molded from an organosheet (a semi-finished pre-impregnated mat); the composite anisotropic tensile properties are evaluated as a function of polymer crystalline morphology and fiber orientation state, both controlled by the extent of material flow in the mold. To study these effects, the full mold coverage and partial center charge of organosheet (OS)-80 %, 60 %, 50 %, and 40 % were compression molded to cause varying anisotropic material flow. Tensile specimens were cut out from the molded plates in the flow and transverse direction and tested to compare their effective tensile properties (modulus and strength). The flow-induced morphological changes in a molded composite at the glass fiber bundle microstructure scale and polymer crystalline phases nano-structure were characterized using optical microscopy and X-ray diffraction, respectively. These morphological changes contributed to the significant change in the tensile strength and modulus; a combined experimental/numerical simulation framework was used to segregate the relative contribution of each factor. Experimentally, the tensile modulus increased in the flow direction from 9.6GPa to 14.9GPa for the specimens produced by full mold coverage and OS-50 % coverage mold, respectively. The tensile strength increased from 162 MPa to 254 MPa for the full and OS-60 % mold coverage. On the contrary, the strength and modulus in the transverse direction to the flow showed a significant drop to 35 MPa and 3GPa, respectively, which was attributed to reduced fiber alignment and anisotropy in the PA6 matrix.

The influence of ethics orientation and information technology mastery on performance for agricultural extension worker in Konawe District

This research aimed to examine the impact of ethics orientation and the mastery of information technology on the performance of agricultural extension workers in Konawe District. The study was conducted in Konawe District, Southeast Sulawesi Province, spanning from January to October 2022. The population and sample included all State Civil Servant (ASN) agricultural extension workers in Konawe District, totaling 113 individuals. The study incorporated exogenous variables, namely the ethics orientation and mastery of information technology of agricultural extension workers, and endogenous variables, focusing on the performance of agricultural extension workers. Descriptive and inferential statistical analyses were employed to assess the state of ethics orientation, mastery of information technology, and the performance of agricultural extension workers. The inferential analysis utilized Partial Least Square (PLS), an equation model of Structural Equation Modeling (SEM) based on a variance or component-centered structural equation modeling approach. The findings revealed a significant positive influence of ethics orientation on the performance of agricultural extension workers in Konawe District. Similarly, the mastery of information technology among agricultural extension workers significantly positively affected their performance in Konawe District.

Molecular dynamics simulation of the effect of tool parameters on nano-cutting of polycrystalline γ-TiAl alloys

As one of the most promising lightweight high-temperature structural materials in the future, the surface quality of γ-TiAl alloys has a great influence on the performance of the workpieces, and the tool parameters are an important factor affecting the machining results. In this study, molecular dynamics simulations of the nano-cutting process of polycrystalline γ-TiAl alloys with different tool parameters were carried out. The results show that increasing the tool rake angle and decreasing the tool edge radius within a certain range helps to reduce the average cutting force, cutting force fluctuation, cutting temperature, and stabilize the cutting process, while the change of the tool clearance angle has less influence on the cutting process. In contrast, negative rake angle cutting is more likely to produce grain rotation and grain boundary steps in the processed substrate and increase the processed surface roughness than positive rake angle cutting; increasing the tool rake angle within a certain range will weaken the elastic recovery effect of the substrate. During cutting at a positive rake angle, whether a portion of the substrate is prone to slip toward the surface of the substrate, thereby reducing the surface quality, depends on the relative state of grain orientation and force applied in the substrate.

Single-cell enzymatic cascade synthesis of testolactone enabled by engineering of polycyclic ketone monooxygenase and multi-gene expression fine-tuning

The synthesis of steroids is challenging through multistep steroidal core modifications with high site-selectivity and productivity. In this work, a novel enzymatic cascade system was constructed for synthesis of testolactone by specific C17 lactonization/Δ1-dehydrogenation from inexpensive androstenedione using an engineered polycyclic ketone monooxygenase (PockeMO) and an appropriate 3-ketosteroid-Δ1-dehydrogenase (ReKstD). The focused saturation mutagenesis in the substrate binding pocket was implemented for evolution of PockeMO to eliminate the bottleneck effect. A best mutant MU3 (I225L/L226V/L532Y) was obtained with 20-fold higher specific activity compared to PockeMO. The catalytic efficiency (kcat/Km) of MU3 was 171-fold higher and the substrate scope shifted to polycyclic ketones. Molecular dynamic simulations suggested that the activity was improved by stabilization of the pre-lactonization state and generation of productive orientation of 4-AD mediated by distal L532Y mutation. Based on that, the three genes, MU3, ReKstD and a ketoreductase for NADPH regeneration, were rationally integrated in one cell via expression fine-tuning to form the efficient single cell catalyst E. coli S9. The single whole-cell biocatalytic process was scaled up and could generate 9.0 g/L testolactone with the high space time yield of 1 g/L/h without steroidal by-product, indicating the potential for site-specific and one-pot synthesis of steroid.

A temporal graph neural network for cross-scale modelling of polycrystals considering microstructure interaction

Machine learning (ML) based methods have achieved preliminary success in the constitutive modeling for single crystals or homogenized polycrystals with remarkable computational efficiency. However, existing ML-based constitutive models neglect grain-level anisotropy, which limits the accurate analysis of local effects. In the current work, a temporal graph neural network (TGNN) model is proposed to simulate cross-scale deformation behaviors of polycrystals under complex loading conditions, with straightforward consideration of microstructure variation and local interaction. The TGNN-based model, a variant of Linearized Minimal State Cells (LMSCs), extends its scope from macroscopic stress response to the mechanical response and orientation evolution of all grains within the aggregate. Specifically, the polycrystalline microstructure is represented with a graph to incorporate essential features of grains, including the spatial connectivity, crystallographic orientation and deformation state. Graph neural network (GNN) is used to capture the spatial correlation of grains, and the features extracted by the GNN are further processed with LMSCs to account for the history-dependent deformation and microstructure evolution. Moreover, the representative volume element (RVE) simulation with crystal plasticity is performed to provide reliable datasets for model establishment. The proposed model demonstrates high efficiency, accuracy and self-consistency in predicting the strain-stress response and orientation evolution at the scale of both individual grain and the overall aggregate under complex loading cases, such as cyclic loading and arbitrary loading.

Structural phase transitions in multicomponent La0.2Nd0.2Sm0.2Gd0.2RE50.2NbO4 (RE5 = Ho, Y, Tb, Eu, Pr) oxides

AbstractIn this work, the influence of compositional complexity on the structural and thermal properties of multicomponent rare‐earth ortho‐niobates from the La0.2Nd0.2Sm0.2Gd0.2RE50.2NbO4 (RE5 = Ho, Y, Tb, Eu, Pr) series was investigated. Based on X‐ray powder diffraction studies using synchrotron radiation, it was found that all tested materials were pure single‐phase compositions and showed stability in the monoclinic I2/c crystal structure at room temperature. High‐temperature X‐ray powder diffraction studies and dilatometry studies confirmed the presence of a structural phase transition between low‐temperature (I2/c) and high‐temperature (I41/a) polymorphs. The structural phase transition temperatures are between 676°C and 701°C. Interestingly, despite their compositional complexity, the structural phase transition temperature behaves similarly to conventional ortho‐niobates, that is, it depends on the radius of the A‐cation; that is, as the ionic radius increases, the phase transition temperature decreases. The transition has been categorized as a second‐order phase transition based on the observed relationship between the Landau order parameter and spontaneous strain. The coexistence of the tetragonal and monoclinic phases has been seen in all compositions around the temperature of the structural phase transition. The presence of two orientation states in the monoclinic structure leads to the so‐called spontaneous strain, which consists of longitudinal (u) and shear (v) strain components. The values of these strains at 300°C range between 2.42 and 2.58 × 10−2 for longitudinal, 2.98 and 3.04 × 10−2 for shear, and 5.46 and 5.57 × 10−2 for scalar spontaneous strain. It was found that the spontaneous strain in each of the materials under test was very little impacted by the variation in the complexity of the A sublattice's composition. In addition, thermal expansion coefficients of both polymorphs were determined, which range from 12.7 × 10−6 K−1 to 13.2 × 10−6 K−1 for the monoclinic structure and 9.7 × 10−6 K−1 to 9.9 × 10−6 K−1 for the tetragonal one.

Machine learning assisted discovery of effective viscous material laws for shear-thinning fiber suspensions

AbstractIn this article, we combine a Fast Fourier Transform based computational approach and a supervised machine learning strategy to discover models for the anisotropic effective viscosity of shear-thinning fiber suspensions. Using the Fast Fourier Transform based computational approach, we study the effects of the fiber orientation state and the imposed macroscopic shear rate tensor on the effective viscosity for a broad range of shear rates of engineering process interest. We visualize the effective viscosity in three dimensions and find that the anisotropy of the effective viscosity and its shear rate dependence vary strongly with the fiber orientation state. Combining the results of this work with insights from literature, we formulate four requirements a model of the effective viscosity should satisfy for shear-thinning fiber suspensions with a Cross-type matrix fluid. Furthermore, we introduce four model candidates with differing numbers of parameters and different theoretical motivations, and use supervised machine learning techniques for non-convex optimization to identify parameter sets for the model candidates. By doing so, we leverage the flexibility of automatic differentiation and the robustness of gradient based, supervised machine learning. Finally, we identify the most suitable model by comparing the prediction accuracy of the model candidates on the fiber orientation triangle, and find that multiple models predict the anisotropic shear-thinning behavior to engineering accuracy over a broad range of shear rates.

A subbands study on the resistivity of field-effect CNT-based piezoresistive nanocomposites

In this paper, an analytical model based on the percolation theory has been developed to predict the subbands effect on the effective electrical resistivity of carbon nanotubes (CNT)-based polymer nanocomposites. The CNTs are considered as randomly distributed or aligned channel material in the polymer transmitting electrons through tunneling. The tunneling effect takes into account the electron transmission between each connected pair of CNTs to evaluate electrical resistivity. The modeling approach contains two steps of primary prediction of resistivity and further calculation of CNTs’ displacements and subsequent change of the resistance. A good agreement is found between the analytical model predictions and experimental data when the tunneling behavior was considered in the percolation transition region. The effect of CNT diameter, orientation state, and subbands on the resistivity has been investigated. The results depict that subbands increment is a collateral benefit to the aspect ratio in decreasing the resistivity. The analytical results demonstrate that a random CNT dispersion leads to a decreased piezoresistivity, while an increased strain range depicts a more non-linear behavior.

Combined effects of substrate temperature and post annealing temperature on structural, optical and electrical properties of Sb-doped SnO2 films

Sb-doped SnO2 (ATO) films have received extensive attention as transparent conductive films, but there is still a lack of in-depth and systematic study on combined effects of substrate temperature (Ts) and annealing temperature (Ta) on their transparent conductive properties. In this study, ATO films were prepared using RF magnetron sputtering at RT∼400 ℃ and subsequently annealed at 200–1000 ℃. The results show that the conductive properties of the films enhance (with a decrease in resistivity from 1.15×10-1 to 2.15×10-3 Ω•cm) and the average visible-light transmittance (TVis) decreases (90.16 → 38.24%) with increase of Ts. With the increase of Ta, the conductive properties of the films prepared at different Ts tend to first enhance and then degrade, and the TVis basically shows a trend of first decreasing and then increasing. After annealing at 600 ℃, the films prepared at RT and 100 ℃ can achieve better comprehensive properties (resistivity of 2.50×10-3 and 1.64×10-3 Ω•cm, carrier concentration of 3.35×1020 and 4.93×1020 cm-3, mobility of 7.73 and 7.76 cm2/V•s, sheet resistance of 84 and 55 Ω/sq., and TVis of 86.63 and 79.50%). In order to explore the intrinsic mechanism of the optical and electrical performance of ATO films changing with Ts and Ta, the preferred orientation, crystallinity, and stress state of the films were analyzed by XRD, and their surface morphology was observed by SEM. Meanwhile, the chemical states of Sn, O and Sb as well as Sb content in the films were analyzed by XPS, and their lattice vibration modes of the film were studied by Raman spectra. Finally, the process-structure-properties correlations were established for the ATO films.

Recognition of local fiber orientation state in prepreg platelet molded composites via deep learning

This paper presents a novel deep learning approach for reconstruction of local through-the-thickness fiber orientation distribution (FOD) in discontinuous long-fiber, prepreg-platelet molded composites (PPMC). The thermal-residual strains on composite surfaces are used as inputs to train a fully convolutional neural network, named U-Net, to predict spatially varying, local FOD. High fidelity synthetic data was generated via computational simulation of PPMC with stochastic material orientation state and was used for training the deep learning model. The proposed U-Net model allowed for rapid recognition of PPMC morphology by solving the inverse structural mechanics problem of determining the average fiber orientation through the composite thickness based on the provided surface strain measurements. Upon training and validation, the U-Net deep learning model was deployed to rapidly predict complex distributions of the local through-the-thickness FOD in PPMC.

Electric field influence on the non-zero temperature resistance of carbon nanotube polymer nanocomposites with subbands effect

The effective electrical resistivity of carbon nanotubes (CNT)-based polymer nanocomposites is studied using an analytical model based on the percolation theory. The CNTs are considered as rod-like conductors that are aligned or randomly dispersed in the polymer to transport electrons by tunneling. When assessing non-zero temperature electrical resistivity, the tunneling effect considers the thermally energized electron transmission that occurs between each coupled pair of CNTs by the electrical transport model. The modeling technique consists of three steps: identifying the percolating network from the primary formation of CNTs, initial prediction of resistivity by equivalent resistor network, computation of the displacements of CNTs for the resistance change. Taking into consideration the behavior of tunneling in the percolation transition zone, a good agreement is obtained between the model results and experimental data. The verified code is used to investigate the sensitivity for different orientation states, aspect ratios and mode numbers. The results reveal lower sensitivity for higher mode numbers as collateral with the influence of increasing randomness.

Exploring the Influence of Perceived Usefulness and Perceived Ease of Use on Technology Engagement of Business Administration Instructors

The increasing prevalence of technology in educational settings, driven by advancements in digital tools and changing pedagogical paradigms, has made its integration vital, particularly in business education, to prepare students for success in dynamic environments. The pandemic further accelerated the adoption of flexible learning modalities, emphasizing the necessity for increased adaptability and resilience in traditional learning environments, and underscoring technology's indispensable role in education. The research methodology involves a quantitative descriptive design, utilizing surveys administered to twenty-nine (29) faculty members of the Negros Oriental State University, Main Campus 1. The study used descriptive statistics and structural equation modeling. The findings indicate a strong agreement among respondents regarding the usefulness and ease of utilizing technology in teaching contexts, with mean scores reflecting high levels of agreement across various statements. Notably, instructors perceived technology as highly useful in teaching and generally find technology easy to use. However, the cognitive challenges associated with its integration are acknowledged, particularly concerning the mental effort required. The study reveals widespread adoption of technology for instructional purposes among respondents, with video conferencing, learning management systems, and collaboration platforms being the most commonly used tools. Furthermore, the analysis of the relationship between technology engagement and technology utilization suggests that perceived usefulness and ease of use do not significantly impact engagement with technology in educational settings. The recommendation is to establish technology integration committees, implement clear standards, invest in infrastructure, offer innovation incentives, foster openness to avoid technology hesitancy, integrate technology into curriculum design, facilitate online collaboration, emphasize data privacy, build community partnerships, and prioritize continuous evaluation.