3D Orientation Research Articles

Nonsingular Indirect Boundary Element Method and Multistrategy Particle Swarm Optimization Algorithm Applied to 3D Subsurface Cavity Inversion

ABSTRACTThe inversion of subsurface geological structures is a crucial approach for gaining insights into the internal composition of the earth. In this paper, we propose a novel inversion method combining the nonsingular indirect boundary element method (IBEM) with the multistrategy particle swarm optimization (MSPSO) algorithm, tailored for accurately inverting 3D subsurface cavities. Leveraging the semi‐analytical nature of IBEM offers advantages such as dimensionality reduction, automatic fulfillment of radiation conditions at infinity, and high computational accuracy. Furthermore, to augment global optimization and local search capabilities, an MSPSO algorithm is introduced. Employing multiple optimization strategies enhances particle diversity, accelerates algorithm convergence, and mitigates the risk of local optima. Through the consideration of subsurface cavities with varying parameters, this method quickly identifies the approximate location of the cavity within a wide search range. The final results demonstrate that the proposed method can simultaneously and accurately invert the 3D spatial position, size, and orientation of the cavity.

Mechanical memory based biofabrication of hierarchical elastic cardiac tissue

Mimicking the multilayered, anisotropic, elastic structure of cardiac tissues for controlled guidiance of 3D cellular orientation is essential in designing bionic scaffolds for cardiac tissue biofabrication. Here, a hierarchically organized, anisotropic, wavy and conductive polycaprolactone/Au scaffold was created in a facile fashion based on mechanical memory during fabrication. The bionic 3D scaffold shows good biocompatibility, excellent biomimetic mechanical properties that guide myoblast alignment, support the hyperelastic behavior observed in native cardiac muscle tissue, and promote myotube maturation, which holds potential for cardiac muscle engineering and the establishment of anin vitroculture platform for drug screening.

Extracting the Heterogeneous 3D Structure of Molecular Films Using Higher Dimensional SFG Microscopy.

Ultrathin molecular films are widespread in both natural and industrial settings, where details of the molecular structure such as density, out-of-plane tilt angles, and in-plane directionality determine their physicochemical properties. Many of these films possess important molecular-to-macroscopic heterogeneity in these structural parameters, which have traditionally been difficult to characterize. Here, we show how extending sum-frequency generation (SFG) microscopy measurements to higher dimensionality by azimuthal-scanning can extract the spatial variation in the three-dimensional molecular structure at an interface. We extend the commonly applied theoretical assumptions used to analyze SFG signals to the study of systems possessing in-plane anisotropy. This theoretical framework is then applied to a phase-separated mixed lipid monolayer to investigate the variation in molecular density and 3D orientation across the chirally packed lipid domains. The results show little variation in out-of-plane structure but a distinct micron-scale region at the domain boundaries with a reduction in both density and in-plane ordering.

A Drone-Based Structure from Motion Survey, Topographic Data, and Terrestrial Laser Scanning Acquisitions for the Floodgate Gaps Deformation Monitoring of the Modulo Sperimentale Elettromeccanico System (Venice, Italy)

The structural deformation monitoring of civil infrastructures can be performed using different geomatic techniques: topographic measurements with total stations and levels, TLS (terrestrial laser scanning) acquisitions, and drone-based SfM (structure from motion) photogrammetric surveys, among others, can be applied. In this work, these techniques are used for the floodgate gaps and the rubber joints deformation monitoring of the MOSE system (Modulo Sperimentale Elettromeccanico), the civil infrastructure that protects Venice and its lagoon (Italy) from high waters. Since the floodgates are submerged most of the time and cannot be directly measured and monitored using high-precision data, topographic surveys were performed in accessible underwater tunnels. In this way, after the calculation of the coordinates of some reference points, the coordinates of the floodgate corners were estimated knowing the geometric characteristics of the system. A specific activity required the acquisition of the TLS scans of the stairwells in the shoulder structures of the Treporti barrier because many of the reference points fixed on the structures were lost during the placement of elements on the seabed. They were replaced with new points whose coordinates in the project/as-built reference system were calculated by applying the Procrustean algorithm by means of homologous points. The procedure allowed the estimation of the transformation parameters with maximum residuals of less than 2.5 cm, a value in agreement with the approximation of the real concrete structures built. Using the obtained parameters, the coordinates of the new reference points were calculated in the project reference system. Once the 3D orientation of all caissons in the barrier was reconstructed, the widths of the floodgate gaps were estimated and compared with the designed values and over time. The obtained values were validated in the Treporti barrier using a drone-based SfM photogrammetric survey of the eight raised floodgates, starting from the east shoulder caisson. The comparison between floodgate gaps estimated from topographic and TLS surveys, and those obtained from measurements on the 3D photogrammetric model, provided a maximum difference of 1.6 cm.

Intelligent recognition and automatic localization of pipeline welds based on multi-vision system

Abstract Currently, the leakage detection of spacecraft pipeline welds relies on manual point-by-point inspection using a detection gun, which is inefficient and inadequate for the automation needs of spacecraft production. However, the accurate recognition and precise localization of widely distributed and small pipeline welds are crucial for automated detection. Therefore, this paper proposes a multi-vision detection and localization system that integrates global and local information, considering both comprehensive global 3D search and high-precision local 3D measurement. The improved YOLOv8 model is employed for pipeline weld recognition, which improves the recognition rate of welds. Based on the deep learning recognized and segmented welds, this paper proposes stereo matching and segmentation extraction methods for 3D localization and pipeline orientation determination. Additionally, the system integrates a robot to perform automated point-by-point inspection of welds within the area without collisions. The experimental results demonstrate the effectiveness of the improved YOLOv8 and the proposed methods for 3D weld localization and pipeline orientation determination. The maximum deviation of the spatial distance of fine weld positioning is 0.20 mm, and the repeatability of the 3D coordinates is around 0.1 mm. The system can perform precise localization and detection, meeting the requirements for automatic weld recognition and localization.

Rapid reversion of martensite in low-alloy steel under electropulsing treatment: Exploring feasibility of reversible transformation

Reversibility is an essential feature of martensitic transformation but is always believed impossible in FeC alloys since martensite (α') is unstable, which decomposes and forms cementite (θ) before reversion begins. Here we used electropulsing treatment (EPT) to inhibit θ precipitation and explored the feasibility of reversible transformation without α' decomposition in low-alloy steels (22MnB5 and 35CrMo steels). The reversion behaviors of the electropulsed specimens were analyzed by morphology, orientation, elemental distribution, and final microstructure. The result shows that in low carbon, low-alloy steel (22MnB5 steel), acicular γ abruptly grows through the whole pack without composition change. The prior γ grains are reproduced with the same grain size, shape, and orientation, indicating that inhibiting the θ precipitation does make steels with low carbon content achieve the reversibility of martensitic transformation. This phenomenon mainly results from reducing the effective driving force for θ precipitation by the current. However, in low-alloy steel with higher carbon content (35CrMo steel), 3D orientation distributions reveal that the prior γ grain is divided into equiaxed grains with various orientations and irregular grains with slightly changed orientations. This result suggests that when carbon content increases above a limit, high dislocation densities lead to recrystallization and destroy the original orientation relationship.

Impact of scapula anatomical landmark positioning on scapular orientation using CT-based 3D models: an intra-observer repeatability and inter-observer reproducibility study

HypothesisThe primary objective of this study was to assess intra-observer repeatability and inter-observer reproducibility of the 3D coordinates of a set of scapula anatomical landmarks obtained by manual positioning on conventional supine CT-scan based scapula surface models. The secondary objective was to assess intra-observer repeatability and inter-observer reproducibility of the resulting 3D scapular orientation. It was hypothesized that the 3D scapular orientation reliability would be better or similar than established reliability of intra-operative baseplate positioning (i.e. version and inclination) in reverse total shoulder arthroplasty (rTSA). MethodsThree anatomical landmarks, i.e. acromial angle (AA), inferior angle (IA), and trigonum scapulae (TS), were manually positioned on 81 scapula surface models using updated landmark definitions. These models were obtained by the segmentation of CT-scan images acquired from patients undergoing elective rTSA procedures at the Department of Surgery at Geneva University Hospitals between May 2022 and December 2023. This procedure was repeated 3 times per scapula by 3 independent observers. A set of parameters corresponding to the 3D landmark coordinates (expressed in an average scapula coordinate system) and the 3D scapular orientation (expressed as three angles related to the retraction/protraction, lateral/medial rotation, and internal/external rotation) were computed. Intra-observer repeatability and inter-observer reproducibility were assessed for each of these parameters using an intra-class correlation as a relative reliability metric. Standard error of measurement (SEM) was also reported as an absolute reliability metric. ResultsIntra-observer ICC and inter-observer ICC ranged from poor to moderate for AA, poor to excellent for IA, and poor to excellent for TS. However, low SEM, ranging from 0.4 mm to 2.4 mm for intra-observer repeatability and from 0.2 mm to 1.8 mm for inter-observer reproducibility, were obtained for every coordinate of each anatomical landmark. This results in poor to moderate intra-observer ICC and inter-observer ICC for scapula angular orientation. Again, low SEM, ranging from 0.4° to 0.8° for intra-observer repeatability and 0.4° to 0.6° for inter-observer reproducibility, were obtained, arguing for reliable measurements. Discussion and ConclusionThis study demonstrates that manual positioning of the three scapula anatomical landmarks recommended by the International Society of Biomechanics can be performed in a reliable manner across measures and observers on surface bone models obtained from CT-scan images. However, a clear and standardized definition of these landmarks is needed to ensure consistency across measurements.

Harnessing interpretable and ensemble machine learning techniques for precision fabrication of aligned micro-fibers

Electrospinning is a robust technique for producing micro/nano-scale fibrous structures, influenced by intricate interplays of fluid dynamics, aerodynamics, and electromagnetic forces. Depending on the desired outcome, these fibers can adopt various morphologies, including solid, tubular, concentric, and gradient. Such morphologies are modulated by parameters such as collector configuration, flow rate, voltage, solution properties, and nozzle dimensions. However, the task of modeling and predicting these multifaceted morphologies remains complex. Aligned microfibers with 3D orientation hold promise in tissue engineering, regenerative medicine, and drug delivery, necessitating meticulous control over the fabrication parameters. In our research, we tapped into machine learning (ML) to address these challenges. Classification ML models were designed to predict fibrous patterns—aligned, random, or jet branching—based on determinants like voltage, flow rate, and collector configurations. Notably, the Random Forest (RF) and Support Vector Machine (SVM) models, especially with radial kernel-trick, displayed outstanding predictive capabilities on the test data. Furthermore, regression-based ML was harnessed to discern fiber alignment coherency and inter-fiber distances. Models such as Lasso and Ridge regression elucidated predictive coefficients for these characteristics, while ensemble models, like gradient-boosting (GB) decision trees (DT), showcased prowess in regression scenarios. Key findings spotlighted the significance of parameters like plate gap for alignment coherency and needle-to-collector distance for inter-fiber spacing. As we strive to gain granular control over micro/nano feature morphology in electrospinning, understanding predictor-response dynamics is imperative. Our investigation underscores the essential role of ML in enhancing both qualitative and quantitative precision in fabricating advanced fibrous structures. Moreover, fusing ML with real-time process monitoring offers groundbreaking potential, particularly in Bio-Fabrication, regenerative medicine, and tissue engineering, where high-precision manufacturing remains a top priority.

The chemical composition of secondary organic aerosols regulates transcriptomic and metabolomic signaling in an epithelial-endothelial in vitro coculture

BackgroundThe formation of secondary organic aerosols (SOA) by atmospheric oxidation reactions substantially contributes to the burden of fine particulate matter (PM2.5), which has been associated with adverse health effects (e.g., cardiovascular diseases). However, the molecular and cellular effects of atmospheric aging on aerosol toxicity have not been fully elucidated, especially in model systems that enable cell-to-cell signaling.MethodsIn this study, we aimed to elucidate the complexity of atmospheric aerosol toxicology by exposing a coculture model system consisting of an alveolar (A549) and an endothelial (EA.hy926) cell line seeded in a 3D orientation at the air‒liquid interface for 4 h to model aerosols. Simulation of atmospheric aging was performed on volatile biogenic (β-pinene) or anthropogenic (naphthalene) precursors of SOA condensing on soot particles. The similar physical properties for both SOA, but distinct differences in chemical composition (e.g., aromatic compounds, oxidation state, unsaturated carbonyls) enabled to determine specifically induced toxic effects of SOA.ResultsIn A549 cells, exposure to naphthalene-derived SOA induced stress-related airway remodeling and an early type I immune response to a greater extent. Transcriptomic analysis of EA.hy926 cells not directly exposed to aerosol and integration with metabolome data indicated generalized systemic effects resulting from the activation of early response genes and the involvement of cardiovascular disease (CVD) -related pathways, such as the intracellular signal transduction pathway (PI3K/AKT) and pathways associated with endothelial dysfunction (iNOS; PDGF). Greater induction following anthropogenic SOA exposure might be causative for the observed secondary genotoxicity.ConclusionOur findings revealed that the specific effects of SOA on directly exposed epithelial cells are highly dependent on the chemical identity, whereas non directly exposed endothelial cells exhibit more generalized systemic effects with the activation of early stress response genes and the involvement of CVD-related pathways. However, a greater correlation was made between the exposure to the anthropogenic SOA compared to the biogenic SOA. In summary, our study highlights the importance of chemical aerosol composition and the use of cell systems with cell-to-cell interplay on toxicological outcomes.Graphical

Fiber orientation and orientation factors in steel fiber‐reinforced concrete beams with hybrid fibers: A critical review

AbstractFiber orientation is of paramount importance for the design of fiber‐reinforced concrete (FRC) structural elements, because it markedly influences the postcracking properties of such material. For this reason, structural codes introduce orientation factors which aim to correlate the real mechanical properties of the structural element with the ones determined from standard beams. Although the need of considering fiber orientation in design codes is commonly accepted, the orientation factors are still based on a limited number of research studies, raising the need to better determine fiber orientation to improve the current standards and support the design process of FRC elements. In this research, a steel fiber‐reinforced concrete (SFRC) with a hybrid system of macro and microfibers is steered into a broad range of fiber orientations and cast into standard beams. Besides measuring the mechanical performance of these SFRC beams, three different methods for assessing fiber orientation are employed, namely electromagnetic induction, image analysis, and micro‐computed tomography. The comparison between the outcomes of the different methods provides detailed information about the accuracy and suitability of each method, considering the corresponding domain of applicability at structural level. Finally, a critical review of the most common 2D and 3D orientation parameters found in literature is performed, and the equations are adapted to account for the hybrid system of fibers.

Role of microstructure on the development of local orientation gradients in polycrystals

In this work, we study the relationship between slip localizations and local orientation gradients in a polycrystalline material. We employ a 3D spatially resolved crystal plasticity-based micromechanics technique that includes explicit intragranular slip band modeling, called SB-FFT (slip band fast Fourier transform). EBSD analysis reveals the development of slip localizations, with a rare few generating relatively large local orientation gradients in the neighboring grain. Our results find that several conditions need to be met simultaneously for large zone of intense orientation gradients to form within a grain and with this, offer an explanation for their scarcity. These conditions entail the localization of slip to a sufficient high intensity in the grain neighbor, the grain experiencing higher stress levels than the average stress of its nearest grain neighborhood, and a crystallographic orientation suitable for activating secondary slip under the stress field produced by the slip localization. Analysis of the 3D orientation distributions as they evolve in strain indicates that orientation gradient zones cause the distribution to become highly skewed, with the extreme tails extending further as strain increases. The numerical simulations with various grain neighborhoods show that this feature is, however, not a sufficient signature for such orientation gradients associated with slip localizations since triple junctions or quadruple points can also produce intragranular orientation gradients that have a similar impact on the grain orientation distribution. Last, comparison with several previously proposed slip transmission criteria indicates that this phenomenon is not relevant, contrary to current belief.

Relative profile measurements in 1.5T MR-linacs: investigation of central axis deviations

Objective. In 1.5 T MR-linacs, the absorbed dose central axis (CAX) deviates from the beam's CAX due to inherent profile asymmetry. In addition, a measured CAX deviation may be biased due to potential lateral (to the beam) effective point of measurement (EPOML) shifts of the detector employed. By investigating CAX deviations, the scope of this study is to determine a set ofEPOMLshifts for profile measurements in 1.5 T MR-linacs.Approach. The Semiflex 3D ion chamber and microDiamond detector (PTW, Germany) were considered in the experimental study while three more detectors were included in the Monte Carlo (MC) study. CAX deviations in the crossline and inline profiles were calculated based on inflection points of the 10×10 cm2field, at five centers. In MC simulations, the experimental setup was reproduced. A small water voxel was simulated to calculate CAX deviation without the impact of the detector-specificEPOMLshift.Main results. All measurements were consistent among the five centers. MC-based and experimental measurements were in agreement within uncertainties. Placing the microDiamond in the vertical orientation does not appear to affect the detector'sEPOML, which is on its central longitudinal axis. For the Semiflex 3D in the crossline direction, the CAX deviation was 2.3 mm, i.e. 1 mm larger than the ones measured using the microDiamond and simulated considering the ideal water detector. Thus, anEPOMLshift of 1 mm is recommended for crossline profile measurements under both Semiflex 3D orientations. For the inline profile, anEPOMLshift of -0.5 mm was determined only for the parallel configuration. In the MC study, CAX deviations were found detector- and orientation-dependent. The dead volume is responsible for theEPOMLshift only in the inline profile and under the parallel orientation.Significance. This work contributes to data availability on the correction or mitigation of the magnetic field-induced changes in the detectors' response.

A laboratory method to determine 3D fibre orientation around knots in sawn timber: case study on a Douglas fir specimen

The mechanical properties of structural timber largely depend on the occurrence of knots and on fibre deviation in their vicinities. In recent strength grading machines, lasers and cameras are used to detect surface characteristics such as the size and position of knots and local fibre orientation. Since laser dot scanning only gives reliable information about the fibre orientation in the plane of board surfaces, simple assumptions are usually made to define the inner fibre orientation to model timber boards. Those models would be improved by better insight into real fibre deviation around knots. In the present work, a laboratory method is developed to evaluate growth layers geometries and fibre orientation, solely based on the fact that the fibers are parallel to the tree rings and without any further assumptions. The method simply relies on color scans and laser dot scans of Douglas fir (Pseudotsuga menziesii) timber specimen sections revealed by successive planing. The proposed method provides data on fibre orientation in 3D with an accuracy that is relevant for the calibration of detailed models.

Three-dimensional fiber orientation mapping of the human brain at micrometer resolution.

The accurate measurement of three-dimensional (3D) fiber orientation in the brain is crucial for reconstructing fiber pathways and studying their involvement in neurological diseases. Comprehensive reconstruction of axonal tracts and small fascicles requires high-resolution technology beyond the ability of current in vivo imaging (e.g. diffusion magnetic resonance imaging). Optical imaging methods such as polarization-sensitive optical coherence tomography (PS-OCT) and polarization microscopy can quantify fiber orientation at micrometer resolution but have been limited to two-dimensional in-plane orientation or thin slices, preventing the comprehensive study of connectivity in 3D. In this work we present a novel method to quantify volumetric 3D orientation in full angular space with PS-OCT. We measure the polarization contrasts of the brain sample from two illumination angles of 0 and 15 degrees and apply a computational method that yields the 3D optic axis orientation and true birefringence. We further present 3D fiber orientation maps of entire coronal cerebrum sections and brainstem with 10 μm in-plane resolution, revealing unprecedented details of fiber configurations. We envision that our method will open a promising avenue towards large-scale 3D fiber axis mapping in the human brain as well as other complex fibrous tissues at microscopic level.

Affordable 3D Orientation Visualization Solution for Working Class Remotely Operated Vehicles (ROV).

ROV operators often encounter challenges with orientation awareness while operating underwater, primarily due to relying solely on 2D camera feeds to manually control the ROV robot arm. This limitation in underwater visibility and orientation awareness, as observed among Malaysian ROV operators, can compromise the accuracy of arm placement, and pose a risk of tool damage if not handle with care. To address this, a 3D orientation monitoring system for ROVs has been developed, leveraging measurement sensors with nine degrees of freedom (DOF). These sensors capture crucial parameters such as roll, pitch, yaw, and heading, providing real-time data on the ROV's position along the X, Y, and Z axes to ensure precise orientation. These data are then utilized to generate and process 3D imaging and develop a corresponding 3D model of the operational ROV underwater, accurately reflecting its orientation in a visual representation by using an open-source platform. Due to constraints set by an agreement with the working class ROV operators, only short-term tests (up to 1 min) could be performed at the dockyard. A video demonstration of a working class ROV replica moving and reflecting in a 3D simulation in real-time was also presented. Despite these limitations, our findings demonstrate the feasibility and potential of a cost-effective 3D orientation visualization system for working class ROVs. With mean absolute error (MAE) error less than 2%, the results align with the performance expectations of the actual working ROV.

A Model for Melt‐Preferred Orientation and Permeabilities in Deformed Partially Molten Peridotites

AbstractIn a deforming partially molten rock, melt concentrates into a grain‐scale melt pocket aligned at a preferred orientation (melt‐preferred orientation, or MPO). However, observing this texture alone provides limited information on the 3D orientation and geometry of these melt pockets, which are critical parameters for estimating permeability. Here, we modeled the MPO of experimentally deformed peridotites by simulating melt streaks arising from melt pockets of various shapes and 3D orientations. The model aims to identify 3D distribution and characteristics of melt pockets that could account for the observed length, thickness, and the probability of melt streaks. Results show that melt pockets at preferred orientation exhibit greater length, thickness, and number density compared to those perpendicular. These results can be incorporated into the simulation of melt flow through individual melt pockets, which allows us to estimate the permeability corresponding to the observed MPO. We found that the permeability of vertically compressed peridotites increases with increasing compressive strain and a more elongated and thickened shape for melt pocket aligned at preferred orientation. The vertical permeability in the sample with 30% compressive strain is at least 40 times larger than that of an undeformed sample. For peridotites deformed under simple shear, the permeability exhibits an anisotropy of at least three. Such anisotropic permeability, coupled with the formation of melt‐rich bands and other melt channels, is believed to cause lateral melt focusing beneath mid‐ocean ridges.

Methods for three-dimensional characterization of the acetabulum prior to pelvic reorientation osteotomy: a scoping review.

Periacetabular osteotomy is the gold standard treatment for acetabular dysplasia. The great variability of acetabular dysplasia requires a personalized preoperative planning improved by 3D reconstruction and computer-assisted surgery. To plan the displacement of the acetabular fragment by a pelvic osteotomy, it is necessary to define a reference plane and a method to characterize 3D acetabular orientation. A scoping review was performed on PubMed to search for articles with a method to characterize the acetabulum of native hips in a 3D reference frame. Ninety-eight articles out of 3815 reports were included. Three reproducible reference planes were identified: the anterior pelvic plane, the Standardization and Terminology Committee plane used in gait analysis, and the sacral base plane. The different methods for 3D analysis of the acetabulum were divided in four groups: global orientation, triplanar measurements, segmentation, and surface coverage of the femoral head. Two methods were found appropriate for reorientation osteotomies: the global orientation by a vector method and the triplanar method. The global orientation method relies on the creation of a vector from the acetabular rim, from the acetabular surface or from successive planes. Normalization of the global acetabular vector would correct acetabular dysplasia by a single alignment maneuver on an ideal vector. The triplanar method, based on angle measurements at the center of the femoral head, would involve correction of anomalies by considering axial, frontal, and sagittal planes. Although not directly fit for reorientation, the two others would help to candidate patients and verify both planning and postoperative result.

Fixed-Wing UAV Pose Estimation Using a Self-Organizing Map and Deep Learning

In many Unmanned Aerial Vehicle (UAV) operations, accurately estimating the UAV’s position and orientation over time is crucial for controlling its trajectory. This is especially important when considering the landing maneuver, where a ground-based camera system can estimate the UAV’s 3D position and orientation. A Red, Green, and Blue (RGB) ground-based monocular approach can be used for this purpose, allowing for more complex algorithms and higher processing power. The proposed method uses a hybrid Artificial Neural Network (ANN) model, incorporating a Kohonen Neural Network (KNN) or Self-Organizing Map (SOM) to identify feature points representing a cluster obtained from a binary image containing the UAV. A Deep Neural Network (DNN) architecture is then used to estimate the actual UAV pose based on a single frame, including translation and orientation. Utilizing the UAV Computer-Aided Design (CAD) model, the network structure can be easily trained using a synthetic dataset, and then fine-tuning can be done to perform transfer learning to deal with real data. The experimental results demonstrate that the system achieves high accuracy, characterized by low errors in UAV pose estimation. This implementation paves the way for automating operational tasks like autonomous landing, which is especially hazardous and prone to failure.

Molecular Orientation Regulation of Hole Transport Semicrystalline-Polymer Enables High-Performance Carbon-Electrode Perovskite Solar Cells.

Carbon-based perovskite solar cells (PSCs) coupled with solution-processed hole transport layers (HTLs) have shown potential owing to their combination of low cost and high performance. However, the commonly used poly(3-hexylthiophene) (P3HT) semicrystalline-polymer HTL dominantly shows edge-on molecular orientation, in which the alkyl side chains directly contact the perovskite layer, resulting in an electronically poor contact at the perovskite/P3HT interface. The study adopts a synergetic strategy comprising of additive and solvent engineering to transfer the edge-on molecular orientation of P3HT HTL into 3D molecular orientation. The target P3HT HTL possesses improved charge transport as well as enhanced moisture-repelling capability. Moreover, energy level alignment between target P3HT HTL and perovskite layer is realized. As a result, the champion devices with small (0.04 cm2) and larger areas (1 cm2) deliver notable efficiencies of 20.55% and 18.32%, respectively, which are among the highest efficiency of carbon-electrode PSCs.

Reconstruction Of The Shape Of Irregular Rough Particles From Their Interferometric Images Using A Convolutional Neural Network

We have developed a convolutional neural network (CNN) to reconstruct the shape of irregular rough particles from their interferometric images. The CNN is based on a UNET architecture with residual block modules. The database has been constructed using the experimental patterns generated by perfectly known pseudo-particles programmed on a Digital Micromirror Device (DMD) and under laser illumination. The CNN has been trained on a basis of 18000 experimental interferometric images using the AUSTRAL super computer (at CRIANN in Normandy). The CNN is tested in the case of centrosymmetric (stick, cross, dendrite) and non-centrosymmetric (like T, Y or L) particles. The size and the 3D orientation of the programmed particles are random. The different shapes are reconstructed by the CNN with good accuracy. Using three angles of view, the 3D reconstruction of particles from three reconstructed faces can be further done.