2D Samples Research Articles

Releasing Charge Transport Barriers at Low Bias in Perpendicularly Aligned 2D Halide Perovskite

2D halide perovskites have garnered significant interest due to the great potential in optoelectronic devices. The 2D Perovskites samples are structured as n layers of [MX6]4− octahedral sheets inducing strong quantum confinement effects. Here, we have synthesized a series of 2D (BA)2MAn-1PbnI3n+1 and its 3D counterpart by controlling the particles alignment to the substrate. We have performed a comprehensive study involving electrical transport and photoconductivity effect as a function of the particle's organization: parallel, perpendicular, and disordered orientation to the electrodes. We have observed that for intermediary value of n, the 2D layered particles are aligned perpendicular to the electrode and parallel to the transport electrical current. A distinct and complex S-shape behavior in V-i curves is disclosed, suggesting two electrical resistance regimes. The sharp increase in resistance suggests the presence of electronic barriers for all samples, which is suppressed for columnar arrays of n = 2 and 3 under illumination. We suggest that this linear behavior in the photoconductivity effect is close related to the absence of electrical barriers at the interface of the particles due to perpendicular alignment to the electrodes. The absence of interfaces in a more columnar array facilitates ionic conductivity enhancing photoelectric effect. Our findings reveal distinct differences in charge transport for perpendicularly aligned 2D samples, opening up possibilities for important applications at low bias.

Advances in personalized modelling and virtual display of ethnic clothing for intelligent customization

Abstract To enhance the intelligence of the ethnic clothing customization industry and provide online solutions for clothing ordering and fashion shows, this study analyses the latest developments in the fields of digital human models, 3D clothing models, and virtual clothing displays in recent years. This study focuses on analysing the categories and processes of three related technologies in clothing virtual display and compares the advantages and disadvantages of four human body modelling methods. Reshaping the facial features of a 3D body scan model and binding the bones of each part is key to providing a personalized body model. In addition, 3D clothing modelling technology is classified into three methods. The 3D fabric scan data are used to establish realistic seam information and process details simulation and use hybrid modelling methods to virtually sew 2D samples, which can enhance the realism of clothing models. The virtual display platform for ethnic clothing is constructed through layered design based on the Unity3D engine, matching versions and virtual display. The above research ideas can solve bottleneck problems in clothing customization. This has important guiding significance for opening up the ethnic clothing customization market, promoting the intelligence of the customization industry, and promoting ethnic clothing culture.

Simple and low-cost microscopy setup for 3D particle field measurement using incoherent illumination and open-source hardware.

The quantification of 3D particle field is of interest for a vast range of fields. While in-line particle holography (PH) can provide high-resolution measurements of particles, it suffers from speckle noise. Plenoptic imaging (PI) is less susceptible to speckle noises, but it involves a trade-off between spatial and angular resolution, rendering images with low resolution. Here, we report a simple microscopy setup with the goals of getting the strengths of both techniques. It is built with off-the-shelf and cost-effective components including a photographic lens, a diaphragm, and a CCD camera. The cost of the microscopy setup is affordable to small labs and individual researchers. The pupil plane of the proposed setup can be mechanically accessible, allowing us to implement pupil plane modulation and increase the depth of field (DOF) without requiring any additional relay lenses. It also allows us to understand the working principle of pupil plane modulation clearly, benefiting microscopy education. It illuminates the sample (particles) using diffuse white light, and thus avoids the problem of speckle noise. It captures multiple perspective images via pupil plane modulation, without requiring trading off angular and spatial resolution. We validate the setup with 2D and 3D particle samples. RESEARCH HIGHLIGHTS: We report a simple and cost-effective microscopy setup with the goals of getting the strengths of plenoptic imaging and in-line particle holography. It is built with off-the-shelf and cost-effective components. The cost of the microscopy setup is affordable to small labs and individual researchers. The pupil plane of the proposed setup can be mechanically accessible, allowing us to implement pupil plane modulation and increase the DOF without requiring any additional relay lenses. It also allows us to understand the working principle of pupil plane modulation clearly, benefiting microscopy education. It illuminates the sample (particles) using diffuse white light, and thus avoids the problem of speckle noise. It captures multiple perspective images via pupil plane modulation, without requiring trading off angular and spatial resolution. We validate the setup with 2D and 3D particle samples.

Bio-Inspired Micro- and Nano-Scale Surface Features Produced by Femtosecond Laser-Texturing Enhance TiZr-Implant Osseointegration.

Surface design plays a critical role in determining the integration of dental implants with bone tissue. Femtosecond laser-texturing has emerged as a breakthrough technology offering excellent uniformity and reproducibility in implant surface features. However, when compared to state-of-the-art sandblasted and acid-etched surfaces, laser-textured surface designs typically underperform in terms of osseointegration. This study investigates the capacity of a bio-inspired femtosecond laser-textured surface design to enhance osseointegration compared to state-of-the-art sandblasted & acid-etched surfaces. Laser-texturing facilitates the production of an organized trabeculae-like microarchitecture with superimposed nano-scale laser-induced periodic surface structures on both 2D and 3D samples of titanium-zirconium-alloy. Following a boiling treatment to modify the surface chemistry, improving wettability to a contact angle of 10°, laser-textured surfaces enhance fibrin network formation when in contact with human whole blood, comparable to state-of-the-art surfaces. In vitro experiments demonstrate that laser-textured surfaces significantly outperform state-of-the-art surfaces with a 2.5-fold higher level of mineralization by bone progenitor cells after 28 days of culture. Furthermore, in vivo evaluations reveal superior biomechanical integration of laser-textured surfaces after 28 days of implantation. Notably, during abiological pull-out tests, laser-textured surfaces exhibit comparable performance, suggesting that the observed enhanced osseointegration is primarily driven by the biological response to the surface.

Multi-plane denoising diffusion-based dimensionality expansion for 2D-to-3D reconstruction of microstructures with harmonized sampling

Acquiring reliable microstructure datasets is a pivotal step toward the systematic design of materials with the aid of integrated computational materials engineering (ICME) approaches. However, obtaining three-dimensional (3D) microstructure datasets is often challenging due to high experimental costs or technical limitations, while acquiring two-dimensional (2D) micrographs is comparatively easier. To deal with this issue, this study proposes a novel framework called ‘Micro3Diff’ for 2D-to-3D reconstruction of microstructures using diffusion-based generative models (DGMs). Specifically, this approach solely requires pre-trained DGMs for the generation of 2D samples, and dimensionality expansion (2D-to-3D) takes place only during the generation process (i.e., reverse diffusion process). The proposed framework incorporates a concept referred to as ‘multi-plane denoising diffusion’, which transforms noisy samples (i.e., latent variables) from different planes into the data structure while maintaining spatial connectivity in 3D space. Furthermore, a harmonized sampling process is developed to address possible deviations from the reverse Markov chain of DGMs during the dimensionality expansion. Combined, we demonstrate the feasibility of Micro3Diff in reconstructing 3D samples with connected slices that maintain morphologically equivalence to the original 2D images. To validate the performance of Micro3Diff, various types of microstructures (synthetic or experimentally observed) are reconstructed, and the quality of the generated samples is assessed both qualitatively and quantitatively. The successful reconstruction outcomes inspire the potential utilization of Micro3Diff in upcoming ICME applications while achieving a breakthrough in comprehending and manipulating the latent space of DGMs.

An Influence of Actuator Gluing on Elastic Wave Excited in the Structure.

In this article, the practical issues connected with guided wave measurement are studied: (1) the influence of gluing of PZT plate actuators (NAC2013) on generated elastic wave propagation, (2) the repeatability of PZT transducers attachment, and (3) the assessment of the possibility of comparing the results of Laser Doppler Vibrometry (LDV) measurement performed on different 2D samples. The consideration of these questions is crucial in the context of the assessment of the possibility of the application of the guided wave phenomenon to structural health-monitoring systems, e.g., in civil engineering. In the examination, laboratory tests on the web of steel I-section specimens were conducted. The size and shape of the specimens were developed in such a way that they were similar to the elements typically used in civil engineering structures. It was proved that the highest amplitude of the generated wave was obtained when the exciters were glued using wax. The repeatability and durability of this connection type were the weakest. Due to this reason, it was not suitable for practical use outside the laboratory. The permanent glue application gave a stable connection between the exciter and the specimen, but the generated signal had the lowest amplitude. In the paper, the new procedure dedicated to objective analysis and comparison of the elastic waves propagating on the surface of different specimens was proposed. In this procedure, the genetic algorithms help with the determination of a new coordinate system, in which the assessment of the quality of wave propagation in different directions is possible.

Topology optimization based on improved DoubleU-Net using four boundary condition datasets

Topology optimization is extensively employed in engineering design. However, applying deep learning to solve topology optimization problems faces challenges owing to limited training data and model adaptability to various boundary conditions. To address these challenges, this research has employed a solid isotropic material with a penalization method to produce a dataset of 400,000 2D samples covering four boundary conditions. Each boundary condition includes two resolution datasets. Moreover, an improved DoubleU-Net model is proposed for real-time topology optimization, ensuring high-accuracy predictions. On the test dataset, the average Intersection over Union (IoU) accuracies of the proposed model for the four boundary conditions above are 93.26%, 96.71%, 96.35% and 97.38%. This research also investigated the impact of different structural datasets on the model's generalization capability. The experimental results indicate that the model trained on random structures exhibits excellent testing performance and demonstrates robust generalization capability.

Abstract PO4-28-10: Single-Cell Transcriptomic Assessment of Stromal and Immune Crosstalk on Breast Cancer Invasion Using 3D Tumor-On-Chip Model

Abstract Introduction: Our understanding of cancer has evolved over the last decades, with treatments increasingly targeting stromal cells in the tumor microenvironment (TME). The TME comprises transformed cancer cells and various non-cancerous cell types such as fibroblasts, macrophages, and endothelial cells. Despite this understanding, disrupting the tumor-stromal interactions are not targeted mainstream, as the stromal cells in the TME are genetically stable and can induce both beneficial and adverse effect on tumorigenesis. Thus, ex vivo tumor models that can faithfully recapitulate these critical tumor-stroma interactions are required to mechanistically understand the multifaceted reaction occurring within the TME. In this study, we established an innovative and multi-cellular TME on-a-chip model and assessed the synergistic effect of patient-derived Cancer Associated Fibroblast (CAF’s) and macrophages (M𝟇) on breast cancer progression. Materials and Methods: The influence of stromal components on cancer progression was assessed using the microfluidic model that allows spatial organization of various cell types. Tri-culture invasion model was established using Sum159 breast cancer cells, patient-derived CAFs, and THP-1 derived naïve (M𝟇) macrophages. Sum159 mixed with collagen and Matrigel forms the tumor region, while CAF’s and M𝟇 embedded in collagen form the stromal region. Functional and real-time assessments such as migration, morphometric analysis, and proliferation studies were conducted to understand the stromal influence on cancer cells. Gene expression analysis on an array of macrophage activation genes was performed to identify the polarized state of the macrophages after cellular interactions. Finally, Single Cell-RNA sequencing was carried out to unveil the drivers of stromal-immune crosstalk on invasion. Results and Discussion: Using the TME on-a-chip model, we compared cancer cell invasion across 4 different culture conditions namely monoculture (Sum only), co-culture (Sum+CAF, Sum+ M𝟇) and tri-culture (Sum+CAF+ M𝟇). Cancer cell migration was significantly increased in the triculture condition compared to others and stromal components also enhanced cancer cell proliferation. Hierarchical clustering of migration and morphological changes demonstrated that the co-culture conditions grouped together while CAF+ M𝟇 exerted the highest influence on cell migration and clustered separately. To understand the bi-directional communication between tumor and stroma, we assessed the polarized states of naïve macrophages using qPCR. Gene expression analysis showed that tumor-educated macrophages showed characteristics of M1 and M2 phenotypes and communicated reciprocally with tumor cells. Finally, to investigate the molecular mechanics of complex cellular interactions, we performed sc-RNA sequencing on cells extracted from the device and 2D cells (controls). Unsupervised clustering identified 11 different clusters, categorizing cells as Sum159 (tumor cells), CAFs (Stromal), and M𝟇 (Immune) based on cell-specific markers. Notably, we observed a noticeable shift in the clusters of Sum159 cells between 2D and 3D samples, emphasizing the difference between these two cultures. Conclusion: Using the TME on-a-chip, we confirmed the critical influence of microenvironmental components on tumor progression through functional assessment. Gene-expression analysis revealed bi-directional communication between noncancerous stromal components and tumor cells highlighting the importance of microenvironmental crosstalk. Furthermore, sc-RNA sequencing revealed the differences between 2D and 3D cultures. Ongoing work aims to perform transcriptomic and bioinformatic analysis on the sc-RNA sequencing data to gain a deeper understanding of the multifaceted communication within our platform. Citation Format: Kalpana Ravi, Lydia Sakala, Jin Park, Joshua LaBaer, Mehdi Nikkhah. Single-Cell Transcriptomic Assessment of Stromal and Immune Crosstalk on Breast Cancer Invasion Using 3D Tumor-On-Chip Model [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-28-10.

Carrier transport in bulk and two-dimensional Zn2(V,Nb,Ta)N3 ternary nitrides.

Density functional theory-based simulations are applied to study the electronic structures, carrier masses, carrier mobility and carrier relaxation times in bulk and two-dimensional (2D) Zn2(V,Nb,Ta)N3 ternary nitrides. Bulk Zn2(V,Nb,Ta)N3 possess moderate band gap sizes of 2.17 eV, 3.11 eV, and 3.40 eV, respectively. Two-dimensional Zn2(V,Nb,Ta)N3 have slightly higher band gap sizes of 2.77 eV, 3.33 eV, and 3.23 eV, respectively. Carrier mass, carrier mobility and carrier relaxation time are found to be anisotropic in all the studied structures. Bulk and 2D samples show an order of magnitude higher electron mobility compared to hole mobility. The highest electron mobility in bulk Zn2NbN3 and Zn2TaN3 is about ∼103 cm2 V-1 s-1. Importantly, for 2D Zn2NbN3, an abnormally high electron mobility of 1.67 × 104 cm2 V-1 s-1 is observed, which is not inferior to the highest known electron mobility values in 2D materials. Such a high electron mobility in 2D Zn2NbN3 can be attributed to a strong delocalization of the conduction band minimum, which is responsible for electron transport. Therefore, this work opens up new materials for high performance nanodevices, such as tandem solar cells and field-effect transistors. This study also provides deep physical insights into the nature of carrier transport mechanisms in bulk and 2D Zn2(V,Nb,Ta)N3 ternary nitrides.

Deep learning reduces data requirements and allows real-time measurements in imaging FCS

Imaging fluorescence correlation spectroscopy (FCS) is a powerful tool to extract information on molecular mobilities, actions, and interactions in live cells, tissues, and organisms. Nevertheless, several limitations restrict its applicability. First, FCS is data hungry, requiring 50,000 frames at 1-ms time resolution to obtain accurate parameter estimates. Second, the data size makes evaluation slow. Third, as FCS evaluation is model dependent, data evaluation is significantly slowed unless analytic models are available. Here, we introduce two convolutional neural networks—FCSNet and ImFCSNet—for correlation and intensity trace analysis, respectively. FCSNet robustly predicts parameters in 2D and 3D live samples. ImFCSNet reduces the amount of data required for accurate parameter retrieval by at least one order of magnitude and makes correct estimates even in moderately defocused samples. Both convolutional neural networks are trained on simulated data, are model agnostic, and allow autonomous, real-time evaluation of imaging FCS measurements.

Digital Inheritance of Traditional Mongolian Robes of the Nayman Tribe

Abstract The Mongolian robe is the crystallisation of the wisdom of Mongolian people after a long period of social practice and one of the intangible cultural heritages of humanity. With the gradual disappearance of the nomadic lifestyle in the steppe, it is not easy to protect and pass on traditional Mongolian dress culture and traditional handicraft technology. This study used the digital twin concept to construct a digital twin to try on clothes for a model in a virtual environment, to realise a mirror image presentation of the target physical entity, and to use effective modern technology to protect and pass on the traditional clothing. The experiment used an AlphaM4 3D body scanner to obtain a 3D human body model, as well as reverse engineering technology to process the initial data, such as noise reduction, streamlining, hole patching and smoothing to build a personalised human body model. The traditional black cotton robe of the Nayman tribe researched in the field was mapped by the planar structure design method, and its structure and 2D samples were recovered. A 3D model of the traditional cotton robe of the Nayman tribe was established based on 3D simulation technology to achieve a 3D virtual display effect, enable better dissemination and inheritance, and give a new vitality to non-heritage clothing in the digital era. The research results can provide fresh ideas and technical support for the digital estate and development of other traditional costumes, as well as contribute to the construction of digital resources for intangible cultural heritage.

Competing length scales and 2D versus 3D dimensionality in relatively thick superconducting NbN films

Magneto-transport characteristics of 2D and 3D superconducting layers, in particular, temperature and angular dependences of the upper critical field Hc2, are usually considered to be fundamentally different. In the work, using non-local resistance measurements at temperatures near the normal-to-superconducting transition, we probed an effective dimensionality of nm-thick NbN films. It was found that in relatively thick NbN layers, the thicknesses of which varied from 50 to 100 nm, the temperature effect on Hc2 certainly pointed to the three-dimensionality of the samples, while the angular dependence of Hc2 revealed behavior typical for 2D samples. The seeming contradiction is explained by an intriguing interplay of three length scales in the dimensionally confined superconducting films: the thickness, the Ginzburg–Landau coherence length, and the magnetic-field penetration depth. Our results provide new insights into the physics of superconducting films with an extremely large ratio of the London penetration depth to the Ginzburg–Landau coherence length exhibiting simultaneously 3D isotropic superconducting properties and the 2D transport regime.

Preparation of rose-like Bi2O2CO3 photocatalyst with enhanced performance for photocatalytic degradation of mineral flotation wastewater

Eight kinds of bismuth oxycarbonate (Bi2O2CO3, BOC) with different microscopic morphology were prepared to study the influence of microscopic morphology on its photocatalytic performance. The crystal structure and microscopic morphology of Bi2O2CO3 were regulated by different synthetic recipes and routes. The SEM and TEM show the samples can be classified into two-dimensional (2D) and three-dimensional (3D) Bi2O2CO3 materials. The BOC-8 possesses a rose-like structure and a specific surface area (SBET) of 44.93 m2/g. In the experiments of degrading mineral flotation agent sodium isobutyl xanthate (SIBX). The photocatalytic performance of 3D samples is generally superior to that of 2D samples, and BOC-8 exhibits the best photocatalytic performance. After 90 min of simulated sunlight irradiation, the degradation ratio of SIBX over BOC-8 is 92.41 %. The transient photocurrent-time curves, electrochemical impedance spectroscopy (EIS) and photoluminescence (PL) spectra show that BOC-8 has excellent photogenerated electron and hole separation efficiency. The degradation pathway of SIBX was explored. And the hydroxyl radicals (OH) is the main active oxygen species for SIBX degrading. The enhanced photocatalytic degradation performance of BOC-8 samples originated from the increase of SBET, improved light utilization and more efficient photogenerated charge separation caused by the formation of 3D structure.

A Comparative Study of Electronic, Optical, and Thermoelectric Properties of Zn-Doped Bulk and Monolayer SnSe Using Ab Initio Calculations.

In this study, we explore the effects of Zn doping on the electronic, optical, and thermoelectric properties of α-SnSe in bulk and monolayer forms, employing density functional theory calculations. By varying the doping concentrations, we aim to understand the characteristics of Zn-doped SnSe in both systems. Our analysis of the electronic band structure using (PBE), (SCAN), and (HSE06) functionals reveals that all doped systems exhibit semiconductor-like behavior, making them suitable for applications in optoelectronics and photovoltaics. Notably, the conduction bands in SnSe monolayers undergo changes depending on the Zn concentration. Furthermore, the optical analysis indicates a decrease in the dielectric constant when transitioning from bulk to monolayer forms, which is advantageous for capacitor production. Moreover, heavily doped SnSe monolayers hold promise for deep ultraviolet applications. Examining the thermoelectric transport properties, we observe that Zn doping enhances the electrical conductivity in bulk SnSe at temperatures below 500 K. However, the electronic thermal conductivity of monolayer samples is lower compared to bulk samples, and it decreases consistently with increasing Zn concentrations. Additionally, the Zn-doped 2D samples exhibit high Seebeck coefficients across most of the temperature ranges investigated.

Density-tuned effective metal-insulator transitions in two-dimensional semiconductor layers: Anderson localization or Wigner crystallization

Electrons (or holes) confined in 2D semiconductor layers have served as model systems for studying disorder and interaction effects for almost 50 years. In particular, strong disorder drives the metallic 2D carriers into a strongly localized Anderson insulator (AI) at low densities whereas pristine 2D electrons in the presence of no (or little) disorder should solidify into a Wigner crystal at low carrier densities. Since the disorder in 2D semiconductors is mostly Coulomb disorder arising from random charged impurities, the applicable physics is complex as the carriers interact with each other as well as with the random charged impurities through the same long-range Coulomb coupling. By critically theoretically analyzing the experimental transport data in depth using a realistic transport theory to calculate the low-temperature 2D resistivity as a function of carrier density in 11 different experimental samples covering 9 different materials, we establish, utilizing the Ioffe-Regel-Mott criterion for strong localization, a direct connection between the critical localization density for the 2D metal-insulator transition (MIT) and the sample mobility deep into the metallic state, which for clean samples could lead to a localization density low enough to make the transition appear to be a Wigner crystallization. We believe that the insulating phase is always an effective Coulomb disorder-induced localized AI, which may have short-range WC-like correlations at low carrier densities. Our theoretically calculated disorder-driven critical MIT density agrees with experimental findings in all 2D samples, even for the ultra-clean samples. In particular, the extrapolated critical density for the 2D MIT seems to vanish when the high-density mobility goes to infinity, indicating that transport probes a disorder-localized insulating ground state independent of how low the carrier density might be.

Physically informed machine-learning algorithms for the identification of two-dimensional atomic crystals

After graphene was first exfoliated in 2004, research worldwide has focused on discovering and exploiting its distinctive electronic, mechanical, and structural properties. Application of the efficacious methodology used to fabricate graphene, mechanical exfoliation followed by optical microscopy inspection, to other analogous bulk materials has resulted in many more two-dimensional (2D) atomic crystals. Despite their fascinating physical properties, manual identification of 2D atomic crystals has the clear drawback of low-throughput and hence is impractical for any scale-up applications of 2D samples. To combat this, recent integration of high-performance machine-learning techniques, usually deep learning algorithms because of their impressive object recognition abilities, with optical microscopy have been used to accelerate and automate this traditional flake identification process. However, deep learning methods require immense datasets and rely on uninterpretable and complicated algorithms for predictions. Conversely, tree-based machine-learning algorithms represent highly transparent and accessible models. We investigate these tree-based algorithms, with features that mimic color contrast, for automating the manual inspection process of exfoliated 2D materials (e.g., MoSe2). We examine their performance in comparison to ResNet, a famous Convolutional Neural Network (CNN), in terms of accuracy and the physical nature of their decision-making process. We find that the decision trees, gradient boosted decision trees, and random forests utilize physical aspects of the images to successfully identify 2D atomic crystals without suffering from extreme overfitting and high training dataset demands. We also employ a post-hoc study that identifies the sub-regions CNNs rely on for classification and find that they regularly utilize physically insignificant image attributes when correctly identifying thin materials.

Comparative analysis of morphology 1D and 2D particles effect in starting powders on microstructure and thermoelectric properties of grained Bi2Te2.7Se0.3 compound

Features in microstructure and thermoelectric properties of grained Bi2Te2.7Se0.3 compound, prepared by spark plasma sintering of starting powders with various morphologies, are comparatively examined. The 1D samples, prepared from the starting powder consisting of 1D bar-shaped particles, were weakly-textured (orientation factor is equal to ∼0.07) and preferentially consisted of bar-shaped grains. The 2D samples, prepared from the starting powder consisting of 2D plate-shaped particles, were highly-textured (orientation factor is equal to ∼0.28) and consisted of plate-shaped grains. Both weakly-textured 1D sample and highly-textured 2D sample demonstrated remarkable anisotropy in the behavior of their transport properties (specific electrical resistivity and total thermal conductivity). For highly-textured sample, this anisotropy is a typical phenomenon, resulting from recovering in crystal anisotropy, which is inherent for single-crystalline Bi2Te3-based material. For the weakly-textured 1D sample, the anisotropy in the transport properties is unexpected enough. In this case, anisotropy can be attributed to the anisotropic grain size effect on the transport properties. This effect is due to a different number of grain boundaries, overcome by the electrons or phonons under different measuring orientations. The highest value of the thermoelectric figure-of-merit (∼0.68) was found for the 1D and 2D samples and at perpendicular measuring orientation.

Extrusion-Based 3D Printing of Stretchable Electronic Coating for Condition Monitoring of Suction Cups.

Suction cups (SCs) are used extensively by the industrial sector, particularly for a wide variety of automated material-handling applications. To enhance productivity and reduce maintenance costs, an online supervision system is essential to check the status of SCs. This paper thus proposes an innovative method for condition monitoring of SCs coated with printed electronics whose electrical resistance is supposed to be correlated with the mechanical strain. A simulation model is first examined to observe the deformation of SCs under vacuum compression, which is needed for the development of sensor coating thanks to the 3D printing process. The proposed design involves three circle-shaped sensors, two for the top and bottom bellows (whose mechanical strains are revealed to be the most significant), and one for the lip (small strain, but important stress that might provoke wear and tear in the long term). For the sake of simplicity, practical measurement is performed on 2D samples coated with two different conductive inks subjected to unidirectional tensile loading. Graphical representations together with analytical models of both linear and nonlinear piezoresistive responses allows for the characterization of the inks’ behavior under several conditions of displacement and speed inputs. After a comparison of the two inks, the most appropriate is selected as a consequence of its excellent adhesion and stretchability, which are essential criteria to meet the target field. Room temperature extrusion-based 3D printing is then investigated using a motorized 3D Hyrel printer with a syringe-extrusion modular system. Design optimization is finally carried out to enhance the surface detection of sensitive elements while minimizing the effect of electrodes. Although several issues still need to be further considered to match specifications imposed by our industrial partner, the achievement of this work is meaningful and could pave the way for a new generation of SCs integrated with smart sensing devices. The 3D printing of conductive ink directly on the cup’s curving surface is a true challenge, which has been demonstrated, for the first time, to be technically feasible throughout the additive manufacturing (AM) process.

Photoluminescence Path Bifurcations by Spin Flip in Two-Dimensional CrPS4.

Ultrathin layered crystals of coordinated chromium(III) are promising not only as two-dimensional (2D) magnets but also as 2D near-infrared (NIR) emitters due to long-range spin correlation and efficient transition between high- and low-spin excited states of Cr3+ ions. In this study, we report on the dual-band NIR photoluminescence (PL) of CrPS4 and show that its excitonic emission bifurcates into fluorescence and phosphorescence depending on thickness, temperature, and defect density. In addition to the spectral branching, the biexponential decay of PL transients, also affected by the three factors, could be well described within a three-level kinetic model for Cr(III). In essence, the PL bifurcations are governed by activated reverse intersystem crossing from the low- to high-spin states, and the transition barrier becomes lower for thinner 2D samples because of surface-localized defects. Our findings can be generalized to 2D solids of coordinated metals and will be valuable in realizing groundbreaking magneto-optic functions and devices.

Ensemble learning and test-time augmentation for the segmentation of mineralized cartilage versus bone in high-resolution microCT images

High-resolution non-destructive 3D microCT imaging allows the visualization and structural characterization of mineralized cartilage and bone. Deriving statistically relevant quantitative structural information about these tissues, however, requires automated segmentation procedures, mainly because manual contouring is user-biased and time-consuming. Despite the increased spatial resolution in microCT 3D volumes, automatic segmentation of mineralized cartilage versus bone remains non-trivial since they have similar grayscale values. Our work investigates how reliable 2D segmentation masks can be predicted automatically based on a (set of) convolutional neural network(s) trained with a limited number of manually annotated samples. To do that, we compared different strategies to select the 2D samples to annotate and considered ensemble learning and test-time augmentation (TTA) to mitigate the limited accuracy and robustness resulting from the small number of annotated training samples. We show that, for a fixed amount of annotated image samples, 2D microCT slices to annotate should preferably be selected in distinct 3D volumes, at regular intervals, rather than being grouped in adjacent slices of a same 3D volume. Two main lessons are drawn regarding the use of ensembles or TTA instead of a single model. First, ensemble learning is shown to improve segmentation accuracy and to reduce the mean and standard deviation of the absolute errors in cartilage characteristics obtained with different initializations of the neural network training process. In contrast, TTA appears to be unable to improve the model’s robustness to unlucky initializations. Second, both TTA and ensembling improved the model’s confidence in its predictions and segmentation failure detection.