3D Network Research Articles

Synthesis, structural characterization, water ad‐/de‐sorption isotherm, and CO2 uptakes of a 2D Cu(I) metal organic framework with 1,3,5‐tris(4‐pyridylethynyl)benzene (L) and Tricyanomethanide (tcm−) ligands

AbstractA d10 Cu(I) two‐dimensional (2D) metal–organic framework (MOF) with chemical formulas, {[Cd(L)(tcm)(H2O)]⋅2H2O}n (1) (L = 1,3,5‐tris(4‐pyridylethynyl)benzene; tcm−1 = tricyanomethanide) was synthesized and structurally characterized by single crystal X‐ray diffraction method. In 1, the coordination geometry of Cu(I) ion is distorted tetrahedral coordinated to four N atoms from three L and one tcm− ligands. A 2D puckered honeycomb‐like layer is formed via the bridges of Cu(I) ions and L ligands with tris‐monodentate coordination mode. Two 2D layers are mutually interpenetrated via the π–π stacking interaction between benzene ring of L ligand and pyridyl ring of neighboring L ligand to form a two‐fold interpenetrated 2D net and then arranged orderly to construct its 3D supramolecular network. Thermal stability and in‐situ temperature‐dependent structural variations of 1 are performed by TG analysis associated with temperature‐dependent PXRD measurement. The water vapor ad‐/de‐sorption isotherm and CO2 adsorption isotherm of 1 are measured and discussed.

Hybrid Space Calibrated 3D Network of Diffractive Hyperspectral Optical Imaging Sensor.

Diffractive multispectral optical imaging plays an essential role in optical sensing, which typically suffers from the image blurring problem caused by the spatially variant point spread function. Here, we propose a novel high-quality and efficient hybrid space calibrated 3D network "HSC3D" for spatially variant diffractive multispectral imaging that utilizes the 3D U-Net structure combined with space calibration modules of magnification and rotation effects to achieve high-accuracy eight-channel multispectral restoration. The algorithm combines the advantages of the space calibrated module and U-Net architecture with 3D convolutional layers to improve the image quality of diffractive multispectral imaging without the requirements of complex equipment modifications and large amounts of data. A diffractive multispectral imaging system is established by designing and manufacturing one diffractive lens and four refractive lenses, whose monochromatic aberration is carefully corrected to improve imaging quality. The mean peak signal-to-noise ratio and mean structural similarity index of the reconstructed multispectral images are improved by 3.33 dB and 0.08, respectively, presenting obviously improved image quality compared with a typical Unrolled Network algorithm. The new algorithm with high space calibrated ability and imaging quality has great application potential in diffraction lens spectroscopy and paves a new method for complex practical diffractive multispectral image sensing.

In Situ Constructing Robust Interface by Deep Eutectic Polymeric Electrolyte Enables High Performance Lithium Metal Batteries with High-Loading Cathode.

The low Li+ transport and poor interface have consistently been two major impediments to practical applications of Polyacrylonitrile (PAN)-based composite solid-state electrolytes (PCPE). In this work, a polymerizable deep eutectic electrolyte is meticulously designed with high fluidity which consists of Poly (Ethylene Glycol) Diacrylate (PEGDA), Fluoroethylene Carbonate (FEC), Succinonitrile (SN) and dual salts (LiTFSI/LiDFOB) to promote Li+ transport and ameliorate the interface of PCPE. Inclusion of PEGDA monomers and FEC alters the crystallinity of SN, enhancing the wettability of thick electrode, and formation of polymeric 3D network from polymerization of PEGDA can anchor SN and suppress the side reactions between SN and lithium metal. Consequently, the modified PCPE exhibit an enhanced conductivity of 4.47×10-4Scm-1 with Li-ion transference number of 0.60, and show an excellent lithium stability. LiCoO2(LCO)/SP-PCPE/Li batteries with higher loading (3-4.4V, 6mgcm-2) can work for over 300 cycles at 0.5 C. Even with an ultra-high loading of 16mgcm-2, LCO/SP-PCPE/Li batteries achieve an excellent cycling performance. This work provides new insights into how to construct a robust interface for solid-state batteries with high-loading cathode.

Ab Initio Simulation of Raman Fingerprints of Sulfur/Carbon Copolymer Cathodes During Discharge of Li-S Batteries.

Sulfur/carbon copolymers have emerged as promising alternatives for conventional crystalline sulfur cathodes for lithium-sulfur batteries. Among these, sulfur--n--1,3--diisopropenylbenzene (S/DIB) copolymers, which present a 3D network of DIB molecules interconnected via sulfur chains, have particularly shown a good performance and, therefore, have been under intensive experimental and theoretical investigations. However, their structural complexity and flexibility have hindered a clear understanding of their structural evolution during redox reactions at an atomistic level. Here, by performing state-of-the-art ab initiomolecular dynamics-based Raman spectroscopy simulations, we investigate the spectral fingerprints of S/DIB copolymers arising from local structures during consecutive reactions with lithium. We discuss Raman spectral changes in particular frequency ranges which are common in S/DIB copolymers having shortand those consisting of longer sulfur chains. We also highlight those spectroscopic fingerprints specific to local S/DIB structures containing only short or long sulfur chains. This could help distinguish them experimentally during discharge. Our theoretically predicted results are in a good agreement with experimental Raman measurements on cells at different discharge stages. This work representsan attempt to compute Raman fingerprints ofcopolymer cathodes during battery operationincluding quantum-chemical and finite-temperature effects, and provides a guideline for Raman spectral changes of arbitrary electrodes during discharge.

Engineered self-assembled protein nanosheets for in situ biosynthesis of peptide anchored protein-semiconductor hybrids for efficient hydrogen production

Catalytic hydrogen production using solar energy is of great significance in addressing the global energy crisis. Herein, an efficient artificial photocatalytic hydrogen production system based on hierarchical protein self-assembly is designed and developed by in situ biosynthesis of peptide-anchored protein-semiconductor hybrids. The self-grown protein-CdS quantum dot hybrids stabilize and array orderly by the predesigned bioengineered proteins assembled into monolayer nanosheets, perfectly mimicking both the structure and function of the natural photosynthetic system. By mild deposition of Pt nanoparticles, the photocatalytic hydrogen production performance of the system is further enhanced to 69100 μmolh−1 (Cd2+) g −1, 80 times the catalytic activity of free CdS. Remarkably, the protein 2D network effectively enhances the water solubility, dispersibility, and solution stability of the inorganic catalytic centers, affording efficient photocatalytic hydrogen evolution. The system maintains 91.2 % catalytic efficiency after 30 days. Furthermore, its hydrogen production rate can be artificially regulated between 69100 and 52100 μmolh−1 (per g Cd2+) by the assembly-disassembly process of the protein templates. Thus, our work showcases the importance of the protein template and its assembly in maintaining the structural stability and high catalytic performance of the photocatalytic system and provides promising ideas for the simulation of natural photosynthetic systems.

Using multi‐component carbon composite as current collector in ultra‐battery

AbstractThis study introduces a multi‐component composite as a substitute for lead grids in ultra‐battery structures. The presented composite decreases the weight and simultaneously extends the cycle life of the traditional lead‐acid battery. Overall, UBZCP (the sample containing metal oxides and polyaniline), as the best sample, increases the cyclic stability by 3.3 and 1.4 times under high rate partial state of charge (HRPSoC) mode, compared to Native and UB450 samples. Metal oxides predominantly enhance the electrical conductivity of carbon‐based composites due to catalytic effects in the reduction of graphene oxide during heat treatment. Poly aniline shows considerably positive effect of electrochemical performance due to the high pseudo‐capacitive properties, high interaction of lead ions and the nitrogen in the polymer chains and accordingly hydrogen evolution reaction (HER) inhibition. The synergistic effect of polyaniline and metal oxides makes the best performance in the UBZCP sample achieve. Also, the UBZCP sample represented an enhancement of about 1.21 and 1.39 times in battery life and specific capacity under deep discharge mode compared to the Native sample. As a result, the introduced multi‐component composite making a 3D conductive network, facilitates Pb/PbSO4 reaction reversibility, and reduces the lead sulfate crystal size.

Supramolecular Conductive Hydrogels for Tissue Engineering Applications.

Owing to their unique attributes, including reversibility, specificity, directionality, and tunability, supramolecular biomaterials have evolved as an excellent alternative to conventional biomaterials like polymers, ceramics, and metals. Supramolecular hydrogels, in particular, have garnered significant interest because their fibrous architecture, high water content, and interconnected 3D network resemble the extracellular matrix to some extent. Consequently, supramolecular hydrogels have been used to develop biomaterials for tissue engineering. Supramolecular conductive hydrogels combine the advantages of supramolecular soft materials with the electrical properties of metals, making them highly relevant for electrogenic tissue engineering. Given the versatile applications of these hydrogels, it is essential to periodically review high-quality research in this area. In this review, we focus on recent advances in supramolecular conductive hydrogels, particularly their applications in tissue engineering. We discuss the conductive components of these hydrogels and highlight notable reports on their use in cardiac, skin, and neural tissue engineering. Additionally, we outline potential future developments in this field.

Hierarchical structure Fe@CNFs@Co/C elastic aerogels with intelligent electromagnetic wave absorption

AbstractDeveloping intelligent electromagnetic wave (EMW) absorption materials with real‐time response‐ability is of great significance in complex application environments. Herein, highly compressible Fe@CNFs@Co/C elastic aerogels were assembled through the electrospinning method, achieving EMW absorption through pressure changes. By varying the pressure, the effective absorption bandwidth (EAB) of Fe@CNFs@Co/C elastic aerogels shows continuous changes from low frequency to high frequency. The EAB of Fe@CNFs@Co/C elastic aerogels is 14.4 GHz (3.36–17.76 GHz), which covers 90% of the range of S/C/X/Ku bands. The theoretical simulation indicates that the external pressure prompts a reduction in the spacing between the fiber layers in the aerogels and facilitates the formation of a 3D conductive network with enhanced attenuation ability of EMW. The uniform distribution of metal particles and appropriate layer spacing can effectively optimize the impedance matching to achieve the best EMW absorption performance. This work state clearly that the hierarchically assembled elastic aerogels composed of metal–organic frameworks (MOFs) derivatives and carbon fibers are ideal dynamic EMW absorption materials for intelligent EMW response.image

Deep learning-based automatic contour quality assurance for auto-segmented abdominal MR-Linac contours

Objective.Deep-learning auto-segmentation (DLAS) aims to streamline contouring in clinical settings. Nevertheless, achieving clinical acceptance of DLAS remains a hurdle in abdominal MRI, hindering the implementation of efficient clinical workflows for MR-guided online adaptive radiotherapy (MRgOART). Integrating automated contour quality assurance (ACQA) with automatic contour correction (ACC) techniques could optimize the performance of ACC by concentrating on inaccurate contours. Furthermore, ACQA can facilitate the contour selection process from various DLAS tools and/or deformable contour propagation from a prior treatment session. Here, we present the performance of novel DL-based 3D ACQA models for evaluating DLAS contours acquired during MRgOART.Approach.The ACQA model, based on a 3D convolutional neural network (CNN), was trained using pancreas and duodenum contours obtained from a research DLAS tool on abdominal MRIs acquired from a 1.5 T MR-Linac. The training dataset contained abdominal MR images, DL contours, and their corresponding quality ratings, from 103 datasets. The quality of DLAS contours was determined using an in-house contour classification tool, which categorizes contours as acceptable or edit-required based on the expected editing effort. The performance of the 3D ACQA model was evaluated using an independent dataset of 34 abdominal MRIs, utilizing confusion matrices for true and predicted classes.Main results.The ACQA predicted 'acceptable' and 'edit-required' contours at 72.2% (91/126) and 83.6% (726/868) accuracy for pancreas, and at 71.2% (79/111) and 89.6% (772/862) for duodenum contours, respectively. The model successfully identified false positive (extra) and false negative (missing) DLAS contours at 93.75% (15/16) and %99.7 (438/439) accuracy for pancreas, and at 95% (57/60) and 98.9% (91/99) for duodenum, respectively.Significance.We developed 3D-ACQA models capable of quickly evaluating the quality of DLAS pancreas and duodenum contours on abdominal MRI. These models can be integrated into clinical workflow, facilitating efficient and consistent contour evaluation process in MRgOART for abdominal malignancies.

Bacterial cellulose-graphene oxide composite membranes with enhanced fouling resistance for bio-effluents management

Bacterial cellulose composites hold promise as renewable bioinspired materials for industrial and environmental applications. However, their use as free-standing water filtration membranes is hindered by low compressive strength, fouling, and poor contaminant selectivity. This study investigates the potential of bacterial cellulose-graphene oxide composites membranes for fouling resistance in pressure-driven filtration. Graphene oxide dispersed in poly(ethylene glycol) (PEG-400) is incorporated as a reinforcing filler into 3D network of bacterial cellulose using an in-situ synthesis method. The effect of graphene oxide on in situ fermentation yield and the formation of percolated-network in the composites shows that the optimal membrane properties are reached at a graphene oxide loading of 2 mg/mL. The two-dimensional graphene oxide nanosheets uniformly dispersed into the matrix of bacterial cellulose nanofibers via hydrogen-bonded interactions demonstrated nearly twofold higher water flux (380 L m−2 h−1) with a molecular weight cut-off ranging between 100–200 KDa and a sixfold increase in wet compression strength than pristine BC. When exposed to synthetic organic foulants and bacterial rich feed solutions, the composite membranes showed more than 95% flux recovery. Additionally, the membranes achieved over 95% rejection of synthetic natural organic matter and bacterial rich solutions, showcasing their enhanced fouling resistance and selectivity.

AEFF-SSC: an attention-enhanced feature fusion for 3D semantic scene completion

Abstract Three-dimensional (3D) occupancy perception technology aims to enable autonomous vehicles to observe and understand dense 3D environments. Estimating the complete geometry and semantics of a scene solely from visual images is challenging. However, humans can easily conceive the complete form of objects based on partial key information and their own experience. This ability is crucial for recognizing and interpreting the surrounding environment. To equip 3D occupancy perception systems with a similar capability, a 3D semantic scene completion method called AEFF-SSC is proposed. This method deeply explores boundary and multi-scale information in voxels, aiming to reconstruct 3D geometry more accurately. We have specifically designed an attention-enhanced feature fusion module that effectively fuses image feature information from different scales and focuses on feature boundary information, thereby more efficiently extracting voxel features. Additionally, we introduce a semantic segmentation module driven by a 3D attention-UNet network. This module combines a 3D U-Net network with a 3D attention mechanism. Through feature fusion and feature weighting, it aids in restoring 3D spatial information and significantly improves the accuracy of segmentation results. Experimental verification on the SemanticKITTI dataset demonstrates that AEFF-SSC significantly outperforms other existing methods in terms of both geometry and semantics. Specifically, within a 12.8 m × 12.8 m area ahead, our geometric occupancy accuracy has achieved a significant improvement of 71.58%, and at the same time, the semantic segmentation accuracy has also increased remarkably by 54.20%.

A flexible porous polyimide/copper composite film toward high-mass-loading anodes in lithium-ion batteries

Conventional metal foil current collectors are essential for electronic conduction in lithium-ion batteries, but the planar structure limits the fast transporting of electron and the power density. Herein, a light, conductive, self-extinguishing, and flexible porous polyimide/copper composite film (PIF/Cu) derived from a polyimide foam was fabricated via hot pressing and electroless plating methods. The honeycomb structure was illustrated in PIF/Cu under a 3D X-ray tomography microscope, revealing the successful construction of a 3D conductive network. PIF/Cu-30 possessed an electronic conductivity of as high as 3.6 × 105 S m−1 at a bulk density of 0.26 g cm−3 and exhibited exceptional structural stability even after successive and harsh mechanical loads. Graphite with a mass load of 12 mg cm−2 was loaded into the PIF/Cu-30 to prepare the anode for cell assembly. The cells using PIF/Cu-30 achieved a better performance in capacity retention (98.2 %@60 cycles) than those using traditional Cu foils (55.1 %@60 cycles), which resulted from the fast transport of electrons and Li-ion through the 3D conductive networks in the porous PIF/Cu-30.

On-chip human lymph node stromal network for evaluating dendritic cell and T-cell trafficking

The lymph node paracortex, also known as the T-cell zone, consists of a network of fibroblastic reticular cells (FRCs) that secrete chemokines to induce T-cell and dendritic cell (DC) trafficking into the paracortex. To model the lymph node paracortex, we utilize multi-channel microfluidic devices to engineer a 3D lymph node stromal network from human cultured FRCs embedded in a collagen I-fibrin hydrogel. In the hydrogel, the FRCs self-assemble into an interconnected network, secrete the extracellular matrix proteins entactin, collagen IV, and fibronectin, as well as express an array of immune cell trafficking chemokines. Although the engineered FRC network did not secrete characteristic CCR7-ligand chemokines (i.e. CCL19 and CCL21), human primary TNF-αmatured monocyte-derived DCs, CD45RA+T-cells, and CD45RA-T-cells migrate toward the lymph node stromal network to a greater extent than toward a blank hydrogel. Furthermore, the FRCs co-recruit DCs and antigen-specific T-cells into the lymph node stromal network. This engineered lymph node stromal network may help evaluate how human DCs and T-cells migrate into the lymph node paracortex via CCR7-independent mechanisms.

Unlocking self-discharge: Unveiling the mysteries of electrode-free Zn-MnO2 batteries with advanced in situ techniques in mild acid aqueous electrolytes

We introduce a novel approach to Zinc-MnO2 battery architecture utilizing a 3D network of carbon nanofibers as both current collector and electrode material, promising enhanced performance and longevity for large-scale energy storage. Employing mild aqueous electrolytes, we address the challenge of managing self-discharge, crucial for short-term energy storage. Advanced coupled characterization techniques, including in-situ EQCM (Electrochemical Quartz Crystal Microbalance) and high-resolution optical microscopy, elucidate self-discharge mechanisms across over multiple length scales. Findings reveal that the self-discharge is mainly at the zinc electrode due to concomitant dissolution of Zinc (corrosion) and HER (Hydrogen Evolution Reaction) phenomena. Interestingly, the corrosion current was estimated irrespective of charging protocol and remains consistent, indicating the independence of zinc corrosion kinetics from the length scale. Finally, the morphology of the zinc layer appears to be critical, suggesting that self-discharge is primarily a chemical process. This innovative design strategy offers the potential for high-performance Zinc-MnO2 batteries with extended cycle life to meet the requirements of large-scale energy storage applications.

Advanced Hydrogels: Enhancing Tissue Bioengineering with RGD Peptides and Carbon Nanomaterials.

The advancement of tissue engineering (TE) is driven by the development of scaffolds that mimic the mechanical, spatial, and biological environment of the extracellular matrix (ECM), crucial for regulating cell behavior and tissue repair. Hydrogels, 3D networks of polymer chains, are particularly suited for TE due to their high biocompatibility, ability to mimic tissue water content, facilitate cell migration, sustain growth factor release, and offer controllable physical properties. However, hydrogels mimicking the ECM often face challenges related to cell adhesion due to the absence of specific receptors. This issue can be addressed by incorporating ECM components into the polymer matrix, such as the peptide sequence arginine-glycine-aspartic acid (RGD), known for its role in cell adhesion. Additionally, carbon nanomaterials (CNMs) offer unique physicochemical properties that can improve scaffold-cell interactions. Despite the potential benefits, there are limited reports on their combination. RGD-CNM hydrogels enable a more accurate emulation of the natural cellular environment, enhancing tissue engineering applications. This hybrid approach may promote robust cell adhesion along with exceptional mechanical and electrical properties. This review outlines the potential benefits of these hybrid scaffolds and their synergistic potential, aiming to inspire new research directions in this innovative field.

The Acute and Chronic influence of Exercise on Mitochondrial Dynamics in Skeletal Muscle.

Exercise and nutritional modulation are potent stimuli for eliciting increases in mitochondrial mass and function. Collectively, these beneficial adaptations are increasingly recognized to coincide with improvements to skeletal muscle health. Mitochondrial dynamics of fission and fusion are increasingly implicated as having a central role in mediating aspects of key organelle adaptions that are seen with exercise. Exercise-induced mitochondrial adaptations that dynamics have been implicated in are: 1) Increases to mitochondrial turnover, resulting from elevated rates of mitochondrial synthesis (biogenesis) and degradative (mitophagy) processes. 2) Morphological changes to the 3D tubular network, known as the mitochondrial reticulum, that mitochondria form in skeletal muscle. Notably, mitochondrial fission has also been implicated in coordinating increases in mitophagy, following acute exercise. Further, increased fusion following exercise training promotes increased connectivity of the mitochondrial reticulum and is associated with improved metabolism and mitochondrial function. However, the molecular basis and fashion in which exercise infers beneficial mitochondrial adaptations through mitochondrial dynamics remains poorly understood. This review attempts to highlight recent developments investigating the effects of exercise on mitochondrial dynamics, while attempting to offer a perspective of the methodological refinements and potential variables, such as substrate/glycogen availability, which should be considered going forward.

Construction of cellulose 3D network composite membrane supported by hydroxylated boron nitride with high surface charge density to achieve high-efficiency osmotic energy harvesting

Ion selectivity and mechanical persistence are critical properties of membranes to harvest osmotic energy. Two-dimensional nanofluidic membranes have been studied to enhance those properties, whereas they are usually constrained due to unsatisfactory mass transfer performances. Herein, a ternary three-dimensional membrane (T-CNF/PVDF/BN-OH composite membrane, abbreviated as CPBN) with distinct mechanical strength and high surface charge density (−2.65 mC·m−2) for sustainable osmotic energy harvesting is designed. In CPBN, negatively charged TEMPO-oxidized cellulose acts as a scaffold and constructs a 3D network structure together with the hydroxylated boron nitride (BN-OH) via a self-assembly process. The affluent nano-constrained ion channels effectively enhance the controllability and effectiveness of ion transport. The abundant ionized carboxyl groups extremely facilitate the selective transportation of the cations, while the BN-OH also assist this process, promoting the one-way transmission of cations in the channel. Additionally, the participation of PVDF polymer forms a network interlock structure, which significantly improves mechanical performances and stability in water. The membrane demonstrates a high power density (10.6 W/m2), measured in minimal testing areas, and an average power density around 1 W/m2 in larger areas, with an energy conversion efficiency of up to 30.7 %. This study provides a promising exploration of replacing traditional two-dimensional layered structure with three-dimensional network structure of cellulose for osmotic energy harvesting.

Layer-By-Layer Magnetic Ordering via Idle Spins and the Optical Signature of Jahn-Teller Cr2+ Ions in Sr2Cr(PO4)2.

Blue/turquoise crystals of Sr2CrII(PO4)2 with prismatic shape and edge-length of up to 1 mm were obtained by a vapor-phase moderated solid-state reaction at 1273 K in sealed silica tubes. Its crystal structure was solved and refined from a triply twinned ("trilling") crystal [Pbca (no. 61), Z = 12, a = 10.7064(6) Å, b = 9.2730(5) Å, c = 21.2720(7) Å, R1 = 0.038]. Sr2Cr(PO4)2 belongs to the small family of inorganic solids containing divalent chromium, where the rare Cr2+ ions are stabilized by the inductive effect of the phosphate groups. As expected from its d4 (S = 2) electronic configuration, the Jahn-Teller effect (JT) is prominent, leading for the two independent Cr2+ ions to square-pyramidal Cr(1)O4+1 and square-planar Cr(2)O4 coordination within a 3D chromium phosphate network [CrII2(PO4)4]8. Topologically, the Cr(1) and Cr(2) cations are arranged in separate alternating layers stacked along the c axis. In their respective layers, Cr(1) shows a gapped 2D topology and only weak interaction with the adjacent Cr(2) layers. However, below TN1 ∼11.3 K, Cr(1) orders antiferromagnetically into a noncollinear structure, leaving nearly paramagnetic Cr(2) idle spins, strongly frustrated by the Cr(1) moments of the next layers. On further cooling, below TN2 ∼3.6 K, the ordering of Cr(2) occurs via an additional magnetic irreducible representation, which splits the Cr(1) into Cr(1)a and Cr(1)b orbits, thus lifting the frustration on Cr(2). The corresponding P21ca.29.99 magnetic space group forces a crystal symmetry lowering, plausibly signed by a change of the magnetostrictive coefficient from positive to negative below TN2. The optical transitions observed for the JT d4 ions are in good agreement with our crystal picture from the DFT calculations. A detailed analysis within the angular overlap model explains the surprisingly different d orbital splitting by the ligand field for the chromophores Cr(1)O4+1 and Cr(2)O4.

Automated volumetric analysis of the inner ear fluid space from hydrops magnetic resonance imaging using 3D neural networks

Due to the development of magnetic resonance (MR) imaging processing technology, image-based identification of endolymphatic hydrops (EH) has played an important role in understanding inner ear illnesses, such as Meniere’s disease or fluctuating sensorineural hearing loss. We segmented the inner ear, consisting of the cochlea, vestibule, and semicircular canals, using a 3D-based deep neural network model for accurate and automated EH volume ratio calculations. We built a dataset of MR cisternography (MRC) and HYDROPS-Mi2 stacks labeled with the segmentation of the perilymph fluid space and endolymph fluid space of the inner ear to devise a 3D segmentation deep neural network model. End-to-end learning was used to segment the perilymph fluid and the endolymph fluid spaces simultaneously using aligned pair data of the MRC and HYDROPS-Mi2 stacks. Consequently, the segmentation performance of the total fluid space and endolymph fluid space had Dice similarity coefficients of 0.9574 and 0.9186, respectively. In addition, the EH volume ratio calculated by experienced otologists and the EH volume ratio value predicted by the proposed deep learning model showed high agreement according to the interclass correlation coefficient (ICC) and Bland–Altman plot analysis.

3D Printable Polymeric Composite Material with Enhanced Conductivity Achieved by Affinity Matching.

Polymer-based conductive composites are lightweight, low-cost, and easily processable materials with important applications in various fields. However, achieving highly conductive and 3D printable polymer-based conductive composites remains challenging. In this study, we successfully developed a highly conductive composite suitable for direct ink writing 3D printing using unsaturated polyester resin as the polymer matrix and graphene nanosheets as conductive fillers and rheological modifiers. Due to the well-matched affinity between graphene nanosheets and unsaturated polyester, the graphene nanosheets aggregate within the unsaturated polyester, forming a 3D conductive network. Moreover, the shearing force during direct ink writing 3D printing induces the orientation of the 2D graphene nanosheets, significantly enhancing their conductivity along the printing direction. At room temperature, the unsaturated polyester resin/graphene composite shows a high conductivity of 69.9 S m-1 while maintaining excellent 3D printability. Structures printed using this material exhibit improved heat dissipation and electromagnetic shielding performance. The reported unsaturated polyester resin/graphene nanosheet composites demonstrate outstanding electrical and heat conductivity and excellent processability, making them promising candidates for applications in electromagnetic shielding, printed electronics, and other fields.