Robust 3D Research Articles

Carbon nanofiber reinforced carbon aerogels for steam generation: Synergy of solar driven interface evaporation and side wall induced natural evaporation

Solar-based interface evaporation (SIE) is a green, efficient and cost-effective technique to harvest fresh water. 3D solar evaporators show their unique advantages in gaining energy from environment and hence possess a higher evaporation rate than 2D evaporators. However, much effort is still required to develop mechanically robust and superhydrophilic 3D evaporators with strong water transportation capability and salt-rejection performance, and at the same time reveal how they gain energy from environment via the natural evaporation. In this work, a novel carbon nanofiber reinforced carbon aerogel (CNFA) is prepared for the SIE. The CNFA has a high light absorption up to 97.2% and outstanding photothermal conversion performance. The heteroatom doping and hierarchically porous structure endow the CNFA with superhydrophilicity and thus powerful water transportation capability and salt rejection performance. Benefiting from synergy of the SIE and side wall induced natural evaporation, the CNFA evaporator exhibits a high evaporation rate and efficiency (as high as 3.82 kg m-2h−1 and 95.5%, respectively) with long-term stability and durability. The CNFA can also work normally in high-salinity and corrosive seawater. This study demonstrates a new method to fabricate all-carbon aerogel solar evaporators and provides insights for the effective thermal management during the interface evaporation.

High energy-power zinc-ion hybrid supercapacitors achieved by 3D channels enriched biomass-derived N/O co-doped 2D arcuate carbon nanosheets

Cost effective and safe zinc-ion hybrid supercapacitors (ZIHSCs), coupling the virtues of aqueous rechargeable supercapacitors and batteries, hold great potential in the energy storage field. However, ZIHSCs development is encumbered by the fatal obstacles of carbon cathodes, including inferior capacity and low energy and power densities. Herein, inspired by the intrinsic feature of well-developed metabolic channels, three-dimensional channels-enriched biomass is effectively converted into a novel N/O co-doped 2D arcuate carbon nanosheets cathode with robust 2D arcuate architecture, an optimized hierarchical porosity plus an ultrahigh surface area and high N/O co-doping contents for high energy-power ZIHSCs to conquer these barriers. The comprehensive integration of these properties affords extensive interfacial active sites for high energy density and fast kinetics for a high power output, thereby endowing remarkable Zn2+ storage capability, including specific capacity of 198.4 mAh/g at 0.2 A/g, capacity retention of 57.2% at a 100-fold enlarged current, and energy and power densities of 155.6 Wh/kg and 18.9 kW/kg, together with no loss in the capacity reservation of 10,000 cycles. Most excitingly, the resultant carbon-based quasi-solid ZIHSC device gave expressive specific capacity of 166.6 mAh/g, outstanding energy density of 123.4 Wh/kg, and excellent mechanical flexibility.

Heteroepitaxy of 2D CuCr2 Te4 with Robust Room-temperature Ferromagnetism.

Magnetic materials in 2D have attracted widespread attention for their intriguing magnetic properties. 2D magnetic heterostructures can provide unprecedented opportunities for exploring fundamental physics and novel spintronic devices. Here, the heteroepitaxial growth of ferromagnetic CuCr2 Te4 nanosheets is reported on Cr2 Te3 and mica by chemical vapor deposition. Magneto-optical Kerr effect measurements reveal the thickness-dependent ferromagnetism of CuCr2 Te4 nanosheets on mica, where a decrease of Curie temperature (TC ) from 320 to 260K and an enhancement of perpendicular magnetic anisotropy with reducing thickness are observed. Moreover, lattice-matched heteroepitaxial ultrathin CuCr2 Te4 on Cr2 Te3 exhibits an enhanced robust ferromagnetism with TC up to 340K due to the interfacial charge transfer. Stripe-type magnetic domains and single magnetic domain are discovered in this heterostructure with different thicknesses. The work provides a way to construct robust room-temperature 2D magnetic heterostructures for functional spintronic devices.

3D Static Modeling and CO2 Static Storage Estimation of the Hydrocarbon-Depleted Charis Reservoir, Bredasdorp Basin, South Africa

An essential greenhouse gas effect mitigation technology is carbon capture, utilization and storage, with carbon dioxide (CO2) injection into underground geological formations as a core of carbon sequestration. Developing a robust 3D static model of the formation of interest for CO2 storage is paramount to deduce its facies changes and petrophysical properties. This study investigates a depleted oilfield reservoir within the Bredasdorp Basin, offshore South Africa. It is a sandstone reservoir with effective porosity mean of 13.92% and dominant permeability values of 100–560 mD (1 mD = 9.869233 × 10–16 m2). The petrophysical properties are facies controlled, as the southwestern area with siltstone and shale facies has reduced porosity and permeability. The volume of shale model shows that the reservoir is composed of clean sands, and water saturation is 10–90%, hence suitable for CO2 storage based on petrophysical characteristics. Static storage capacity of the reservoir as virgin aquifer and virgin oilfield estimates sequestration of 0.71 Mt (million tons) and 1.62 Mt of CO2, respectively. Sensitivity studies showed reservoir depletion at bubble point pressure increased storage capacity more than twice the depletion at initial reservoir pressure. Reservoir pressure below bubble point with the presence of gas cap also increased storage capacity markedly.

An improved deep learning architecture for multi-object tracking systems

Robust and reliable 3D multi-object tracking (MOT) is essential for autonomous driving in crowded urban road scenes. In those scenarios, accurate data association between tracked objects and incoming new detections is crucial. This paper presents a tracking system based on the Kalman filter that uses a deep learning approach to the association problem. The proposed architecture consists of three neural networks. First, a convolutional LSTM network extracts spatiotemporal features from a sequence of detections of the same track. Then, a Siamese network calculates the degree of similarity between all tracks and the new detections found at each new frame. Finally, a recurrent LSTM network is used to extract 3D and bounding box information. This model follows the tracking-by-detection paradigm and has been trained with track sequences to be able to handle missed observations and to reduce identity switches. A validation test was carried out on the Argoverse dataset to validate the performance of the proposed system. The developed deep learning approach could improve current multi-object tracking systems based on classic algorithms like the Kalman filter.

Robust 2D porphyrin metal–organic framework nanosheets for high-efficiency photoreduction-assisted uranium recovery from wastewater

The elimination of highly radioactive and chemically toxic uranium from nuclear waste is of significance for radionuclide remediation and environment protection. Herein, two-dimensional (2D) porphyrin-based nickel (Ni-TCPP) metal–organic frameworks (MOFs) are successfully prepared by a simple surfactant-assisted strategy. Owing to highly accessible active sites and low diffusion barrier, 2D Ni-TCPP nanosheets present fast uranium capture by coordination of uranyl ions with TCPP ligands and Ni paddlewheel metal nodes. Particularly, 2D Ni-TCPP nanosheets display outstanding photocatalytic activity due to the merit of dual ligand-to-metal charge transfer pathways, greatly improving photoreduction-assisted uranium recovery performance. As a result, light-irradiated 2D Ni-TCPP adsorbents display fast removal of uranium from acidic wastewater, excellent selectivity and good reusability. Moreover, a high uranium removal rate can be maintained even in the presence of excess calcium ions. These advantages combined with good structure stability make 2D Ni-TCPP MOFs promising for selective uranium separation in nuclear waste remediation.

Mass-Manufacturable 3D Magnetic Force Sensor for Robotic Grasping and Slip Detection.

The manipulation of delicate objects remains a key challenge in the development of industrial robotic grippers. Magnetic force sensing solutions, which provide the required sense of touch, have been demonstrated in previous work. The sensors feature a magnet embedded within a deformable elastomer, which is mounted on top of a magnetometer chip. A key drawback of these sensors lies in the manufacturing process, which relies on the manual assembly of the magnet-elastomer transducer, impacting both the repeatability of measurements across sensors and the potential for a cost-effective solution through mass-manufacturing. In this paper, a magnetic force sensor solution is presented with an optimized manufacturing process that will facilitate mass production. The elastomer-magnet transducer was fabricated using injection molding, and the assembly of the transducer unit, on top of the magnetometer chip, was achieved using semiconductor manufacturing techniques. The sensor enables robust differential 3D force sensing within a compact footprint (5 mm × 4.4 mm × 4.6 mm). The measurement repeatability of these sensors was characterized over multiple samples and 300,000 loading cycles. This paper also showcases how the 3D high-speed sensing capabilities of these sensors can enable slip detection in industrial grippers.

Enhancing quorum quenching media with 3D robust electrospinning coating: A novel biofouling control strategy for membrane bioreactors

Bacterial quorum quenching (QQ) is an effective strategy for controlling biofouling in membrane bioreactor (MBR) by interfering the releasing and degradation of signal molecules during quorum sensing (QS) process. However, due to the framework feature of QQ media, the maintenance of QQ activity and the restriction of mass transfer threshold, it has been difficult to design a more stable and better performing structure in a long period of time. In this research, electrospun fiber coated hydrogel QQ beads (QQ-ECHB) were fabricated by using electrospun nanofiber coated hydrogel to strengthen layers of QQ carriers for the first time. The robust porous PVDF 3D nanofiber membrane was coated on the surface of millimeter-scale QQ hydrogel beads. Biocompatible hydrogel entrapping quorum quenching bacteria (sp.BH4) was employed as the core of the QQ-ECHB. In MBR with the addition of QQ-ECHB, the time to reach transmembrane pressure (TMP) of 40 kPa was 4 times longer than conventional MBR. The robust coating and porous microstructure of QQ-ECHB contributed to keeping a lasting QQ activity and stable physical washing effect at a very low dosage (10g beads/5L MBR). Physical stability and environmental-tolerance tests also verified that the carrier can maintain the structural strength and keep the core bacteria stable when suffering long-term cyclic compression and great fluctuations in sewage quality.

High-Precision 3D Reconstruction Study with Emphasis on Refractive Calibration of GelStereo-Type Sensors.

GelStereo sensing technology is capable of performing three-dimensional (3D) contact shape measurement under various contact structures such as bionic curved surfaces, which has promising advantages in the field of visuotactile sensing. However, due to multi-medium ray refraction in the imaging system, robust and high-precision tactile 3D reconstruction remains a challenging problem for GelStereo-type sensors with different structures. In this paper, we first propose a universal Refractive Stereo Ray Tracing (RSRT) model for GelStereo-type sensing systems to realize 3D reconstruction of the contact surface. Moreover, a relative geometry-based optimization method is presented to calibrate multiple parameters of the proposed RSRT model, such as the refractive indices and structural dimensions. Furthermore, extensive quantitative calibration experiments are performed on four different GelStereo sensing platforms; the experimental results show that the proposed calibration pipeline can achieve less than 0.35 mm in Euclidean distance error, based on which we believe that the proposed refractive calibration method can be further applied in more complex GelStereo-type and other similar visuotactile sensing systems. Such high-precision visuotactile sensors can facilitate the study of robotic dexterous manipulation.

Multi-scale Edge-guided Learning for 3D Reconstruction

Single-view three-dimensional (3D) object reconstruction has always been a long-term challenging task. Objects with complex topologies are hard to accurately reconstruct, which makes existing methods suffer from blurring of shape boundaries between multiple components in the object. Moreover, most of them cannot balance learning between global geometric structure information and local detail information. In this article, we propose a multi-scale edge-guided learning network (MEGLN) to utilize the global edge information guiding the network to better capture and recover local details. The goal is to exploit the multi-scale learning strategy to learn global edge information and local details, thus achieving robust 3D object reconstruction. We first design a multi-scale Gaussian difference block (MGDB) to extract global edge geometry features for input images of different scales and adopt the attention mechanism to aggregate the extracted global edge geometry features of different scales. Second, we design a multi-scale feature interaction block (MFIB) to learn local details, which utilizes the multi-scale feature interaction to capture the features of multiple objects or components at multiple scales. The MFIB can learn and capture better as much local detail information as possible under the guidance of global edge information. Finally, we dynamically fuse the predicted probabilities of the MGDB and MFIB to obtain the final predicted result, which makes our MEGLN able to recover 3D shapes with global complex topological structures and rich local details via the multi-scale learning strategy. Extensive qualitative and quantitative experimental results on the ShapeNet dataset demonstrate that our approach achieves competitive performance compared with state-of-the-art methods. Code is available at https://github.com/Ray-tju/MEGLN .

Guidelines for Accurate Multi-Temporal Model Registration of 3D Scanned Objects.

Changes in object morphology can be quantified using 3D optical scanning to generate 3D models of an object at different time points. This process requires registration techniques that align target and reference 3D models using mapping functions based on common object features that are unaltered over time. The goal of this study was to determine guidelines when selecting these localized features to ensure robust and accurate 3D model registration. For this study, an object of interest (tibia bone replica) was 3D scanned at multiple time points, and the acquired 3D models were aligned using a simple cubic registration block attached to the object. The size of the registration block and the number of planar block surfaces selected to calculate the mapping functions used for 3D model registration were varied. Registration error was then calculated as the average linear surface variation between the target and reference tibial plateau surfaces. We obtained very low target registration errors when selecting block features with an area equivalent to at least 4% of the scanning field of view. Additionally, we found that at least two orthogonal surfaces should be selected to minimize registration error. Therefore, when registering 3D models to measure multi-temporal morphological change (e.g., mechanical wear), we recommend selecting multiplanar features that account for at least 4% of the scanning field of view. For the first time, this study has provided guidelines for selecting localized object features that can provide accurate 3D model registration for 3D scanned objects.

Constructing gene features for robust 3D mesh zero-watermarking

The rapid developments of information technology and network communication have made mesh models convenient for copying and sharing through the Internet. Accordingly, the protection of the intellectual property has become an urgent and crucial task. No doubt that the robust watermarking technology is a clever way to solve this issue. The fault tolerance, however, is proportional to the amount of data. Hence, it is difficult to counteract attacks which deleting or altering a large amount of vertex information. In this article, we aim to propose a robust zero-watermarking technique based on 3D mesh gene features. The model is divided into multiple gene groups. Recessive and dominant features can be extracted from these gene groups to resist the distortion and distortionless attacks, respectively. Simulations have proved that the gene features can be used to effectively resist attacks of noise addition, cropping, smoothing, simplification, and common lossless operations. Thus, the proposed method can firmly protect the intellectual property rights of the mesh model.

Above-Room-Temperature Ferromagnetism in Thin van der Waals Flakes of Cobalt-Substituted Fe5GeTe2

Two-dimensional (2D) magnetic van der Waals materials provide a powerful platform for studying the fundamental physics of low-dimensional magnetism, engineering novel magnetic phases, and enabling thin and highly tunable spintronic devices. To realize high-quality and practical devices for such applications, there is a critical need for robust 2D magnets with ordering temperatures above room temperature that can be created via exfoliation. Here, the study of exfoliated flakes of cobalt-substituted Fe5GeTe2 (CFGT) exhibiting magnetism above room temperature is reported. Via quantum magnetic imaging with nitrogen-vacancy centers in diamond, ferromagnetism at room temperature was observed in CFGT flakes as thin as 16 nm corresponding to 16 layers. This result expands the portfolio of thin room-temperature 2D magnet flakes exfoliated from robust single crystals that reach a thickness regime relevant to practical spintronic applications. The Curie temperature Tc of CFGT ranges from 310 K in the thinnest flake studied to 328 K in the bulk. To investigate the prospect of high-temperature monolayer ferromagnetism, Monte Carlo calculations were performed, which predicted a high value of Tc of ∼270 K in CFGT monolayers. Pathways toward further enhancing monolayer Tc are discussed. These results support CFGT as a promising platform for realizing high-quality room-temperature 2D magnet devices.

A robust self-referenced 2D nyquist ghost correction for different MRI-biomarker measurements based on multi-band interleaved EPI

Purpose: To enable 2D Nyquist ghost correction for multi-band interleaved echo-planar imaging (EPI) without reference scan.Methods: 2D phase errors between positive and negative echo were directly measured from the multi-band interleaved EPI data acquired with alternating readout polarity, and then corrected by using multiplexed sensitivity encoding (MUSE) framework. The proposed method was tested on brain data acquired with and without multi-band imaging under different multi-shot factors (4, 8, and 16). In addition, the 2D phase corrections for the interleaved EPI data acquired with 2D zoomed-FOV, 2D reduced-FOV, or 3D multi-band imaging were also performed with the proposed method. The performance of our proposed method was assessed with single-to-noise ratio (SNR) and ghost-to-signal ratio (GSR), and then compared with an iterative 2D phase correction method. The g-factor penalty of the proposed 2D Nyquist ghost correction method associated with 2D phase error was also evaluated. The feasibility of the proposed method was evaluated in diffusion MRI and multi-echo fMRI where quantitative biomarkers with high quality are desired.Results: The proposed method successfully suppressed the ghost artifacts for the multi-shot interleaved EPI data acquired with different multi-shot factors, 2D and 3D multi-band acquisitions, zoomed-FOV, or reduced-FOV. Compared with the iterative method, our proposed method showed lower GSR and comparable SNR performance. Through g-factor penalty simulation, the proposed method showed less than 9% of overall SNR loss associated with a maximum y-linear phase error of π. The residual aliasing artifact was effectively eliminated in the quantitative biomarkers (ADC, FA and dynamic T2*) when applying the proposed method in diffusion imaging and multi-echo fMRI.Conclusion: The proposed method can perform robust 2D Nyquist ghost correction for multi-band multi-shot interleaved EPI without reference scan, and thus can be a generalized 2D ghost correction method for multi-shot interleaved EPI based acquisitions and applications.

Strong Antibonding I (p)-Cu (d) States Lead to Intrinsically Low Thermal Conductivity in CuBiI4.

Chemical bonding present in crystalline solids has a significant impact on how heat moves through a lattice, and with the right chemical tuning, one can achieve extremely low thermal conductivity. The desire for intrinsically low lattice thermal conductivity (κlat) has gained widespread attention in thermoelectrics, in refractories, and nowadays in photovoltaics and optoelectronics. Here we have synthesized a high-quality crystalline ingot of cubic metal halide CuBiI4 and explored its chemical bonding and thermal transport properties. It exhibits an intrinsically ultralow κlat of ∼0.34-0.28 W m-1 K-1 in the temperature range 4-423 K with an Umklapp crystalline peak of 1.82 W m-1 K-1 at 20 K, which is surprisingly lower than other copper-based halide or chalcogenide materials. The crystal orbital Hamilton population analysis shows that antibonding states generated just below the Fermi level (Ef), which arise from robust copper 3d and iodine 5p interactions, cause copper-iodide bond weakening, which leads to reduction of the elastic moduli and softens the lattice, finally to produce extremely low κlat in CuBiI4. The chemical bonding hierarchy with mixed covalent and ionic interactions present in the complex crystal structure generates significant lattice anharmonicity and a low participation ratio in low-lying optical phonon modes originating mostly from localized copper-iodide bond vibrations. We have obtained experimental evidence of these low-lying modes by low-temperature specific heat capacity measurement as well as Raman spectroscopy. The presence of strong p-d antibonding interactions between copper and iodine leads to anharmonic soft crystal lattice which gives rise to low-energy localized optical phonon bands, suppressing the heat-carrying acoustic phonons to steer intrinsically ultralow κlat in CuBiI4.

A Parallel Recurrent Neural Network for Robust Inertial and Magnetic Sensor-Based 3D Orientation Estimation

A Parallel Recurrent Neural Network for Robust Inertial and Magnetic Sensor-Based 3D Orientation Estimation

Design of robust fuzzy iterative learning control for nonlinear batch processes.

In this paper, a two-dimensional (2D) composite fuzzy iterative learning control (ILC) scheme for nonlinear batch processes is proposed. By employing the local-sector nonlinearity method, the nonlinear batch process is represented by a 2D uncertain T-S fuzzy model with non-repetitive disturbances. Then, the feedback control is integrated with the ILC scheme to be investigated under the constructed model. Sufficient conditions for robust asymptotic stability and 2D $ H_\infty $ performance requirements of the resulting closed-loop fuzzy system are established based on Lyapunov functions and some matrix transformation techniques. Furthermore, the corresponding controller gains can be derived from a set of linear matrix inequalities (LMIs). Finally, simulations on the three-tank system and the highly nonlinear continuous stirred tank reactor (CSTR) are carried out to prove the feasibility and efficiency of the proposed approach.

Small-molecule screen reveals pathways that regulate C4 secretion in stem cell-derived astrocytes.

In the brain, the complement system plays a crucial role in the immune response and in synaptic elimination during normal development and disease. Here, we sought to identify pathways that modulate the production of complement component 4 (C4), recently associated with an increased risk of schizophrenia. To design a disease-relevant assay, we first developed a rapid and robust 3D protocol capable of producing large numbers of astrocytes from pluripotent cells. Transcriptional profiling of these astrocytes confirmed the homogeneity of this population of dorsal fetal-like astrocytes. Using a novel ELISA-based small-molecule screen, we identified epigenetic regulators, as well as inhibitors of intracellular signaling pathways, able to modulate C4 secretion from astrocytes. We then built a connectivity map to predict and validate additional key regulatory pathways, including one involving c-Jun-kinase. This work provides a foundation for developing therapies for CNS diseases involving the complement cascade.

Targeting Leishmania donovani sterol methyltransferase for leads using pharmacophore modeling and computational molecular mechanics studies

The mortalities and morbidities of leishmaniasis are high and the disease is under reported globally. The absence of vaccines coupled with chemotherapeutic challenges including chemoresistance, scarcity and toxicity have made the fight against leishmaniasis an arduous one. Furthermore, the treatment options currently available for leishmaniasis are long and sometimes require hospitalization. There is therefore the need to explore novel pathways to identify new compounds with alternative mechanisms of action. A pharmacophore-based screening was employed in identifying new potential inhibitors with unique scaffolds targeting Leishmania donovani sterol methyltransferase (LdSMT), a key enzyme for ergosterol biosynthesis. To accomplish this, 22,26-azasterol, a known inhibitor of this target and five other derivatives with IC50 less than 10 μM were used to generate a robust 3D pharmacophore model via LigandScout with a score of 0.9144. The validated model was used as a query to screen a library of 69034 natural products obtained from the InterBioScreen Limited. Compounds with pharmacophore fit scores above 50 were docked against the modelled structure of LdSMT. Altogether, ten molecules with binding energies between −7 and −11 kcal/mol were identified as potential bioactive molecules. The molecular dynamics simulation and molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) calculations reinforced the results from the docking studies suggesting the selected hits bind effectively at the active sites of the target protein. The compounds were observed to bind in the S-adenosine-L-homocysteine binding pocket of the modelled LdSMT with Trp208 and Val330 predicted as key residues critical for ligand binding. Prediction of biological activity with probability of activity (Pa) greater than probability of inactivity (Pi) revealed that seven compounds (STOCKIN-54848, STOCKIN-89115, STOCKIN-68720, STOCKIN-44724, STOCKIN-76694, STOCKIN-47277 and STOCKIN-95708) possessed antileishmanial properties. STOCKIN-89115, STOCKIN-68720, STOCKIN-44724, and STOCKIN-47277 were predicted to be membrane permeability inhibitors, while all ten hit compounds possessed antineoplastic activity. The compounds have the propensity of disrupting ergosterol biosynthesis leading to the suppression of growth in Leishmania donovani. The compounds were predicted to have good absorption, distribution, metabolism, excretion and toxicity profiles, hence their potential antileishmanial activity can be exploited upon experimental corroboration.

The shape-retention and heat-tolerant elastic helix based on azobenzene-polyimide supramolecular assembly

Helical structures are ubiquitous in natural and engineered systems across multiple length scales, while they often exhibit nearly uniform radius and pitch. Utilization of biomimetic structure design to develop stretchable and robust 3D helical structures that are shape-preserving and heat-tolerant is of great interest. Herein, we devise flexible 3D helixes by supramolecular self-assembly and high voltage electric field orientation of molecular chains. The 3D helixes not only inherit buffering mechanics with exceptional shape-retention ability against cyclic stretching but also exhibit excellent heat-resistant quality. The results of the experimental tests confirm that the helix can recover its original shape without obvious residual strain and basically withstand after 2000 cycles under force loading of 10 cN. Such robust biomimetic materials hold great prospects in the fields of artificial muscles, wearable materials, and so on.