State of the art, challenges, and future prospects for the multi-material 3D printing of plant-based meat.

The emergence of innovative plant-based meat analogs, replicating the flavor, texture, and appearance of animal meat cuts, is deemed crucial for sustainably feeding a growing population while mitigating the environmental impact associated with livestock farming. Multi-material 3D food printing (MM3DFP) has been proposed as a potentially disruptive technology for manufacturing the next generation of plant-based meat analogs. This article provides a comprehensive review of the state of the art, addressing various aspects of 3D printing in the realm of plant-based meat. The disruptive potential of printed meat analogs is discussed with particular emphasis on protein-rich, lipid-rich, and blood-mimicking food inks. The printing parameters, printing requirements, and rheological properties at the different printing stages are addressed in detail. As food rheology plays a key role in the printing process, an appraisal of this subject is performed. Post-printing treatments are assessed based on the extent of improvement in the quality of 3D-printed plant-based meat analogs. The meat-mimicking potential is revealed through sensory attributes, such as texture and flavor. Furthermore, there has been limited research into food safety and nutrition. Economically, the 3D printing of plant-based meat analogs demonstrates significant market potential, contingent upon innovative decision-making strategies and supportive policies to enhance consumer acceptance. This review examines the current limitations of this technology and highlights opportunities for future developments.

The Influence of Weld Interface Characteristics on the Bond Strength of Collision Welded Aluminium-Steel Joints.

Collision welding is a promising approach for joining conventional materials in identical or dissimilar combinations without heat-related strength loss, thereby opening up new lightweight potential. Widespread application of this technology is still limited by an insufficient state of knowledge with respect to the underlying joining mechanisms. This paper applies collision welding to a material combination of DC04 steel and EN AW 6016 aluminium alloy. Firstly, the welding process window for the combination is determined by varying the collision speed and the collision angle, the two main influencing variables in collision welding, using a special model test rig. The process window area with the highest shear tensile strength of the welded joint is then determined using shear tensile tests and SEM images of the weld zone. The SEM investigations reveal four distinct metallographic structures in the weld zones, the area fractions of which are determined and correlated with collision angle and shear tensile strength.

Material Compatibility in 4D Printing: Identifying the Optimal Combination for Programmable Multi-Material Structures.

This study identifies the optimal combination of active and passive thermoplastic materials for producing multi-material programmable 3D structures. These structures can undergo shape changes with varying radii of curvature over time when exposed to hot water. The research focuses on examining the thermal, thermomechanical, and mechanical properties of active (PLA) and passive (PRO-PLA, ABS, and TPU) materials. It also includes the experimental determination of the radius of curvature of the programmed 3D structures. The pairing of active PLA with passive PRO-PLA was found to be the most effective for creating complex programmable 3D structures capable of two-sided transformation. This efficacy is attributed to the adequate apparent shear strength, significant differences in thermomechanical shrinkage between the two materials, identical printing parameters for both materials, and the lowest bending storage modulus of PRO-PLA among the passive materials within the activation temperature range. Multi-material 3D printing has also proven to be a suitable method for producing programmable 3D structures for practical applications such as phone stands, phone cases, door hangers, etc. It facilitates the programming of the active material and ensures the dimensional stability of the passive components of programmable 3D structures during thermal activation.

Prior image-based generative adversarial learning for multi-material decomposition in photon counting computed tomography.

BACKGROUND: Photon counting detector computed tomography (PCD-CT) is a novel promising technique providing higher spatial resolution, lower radiation dose and greater energy spectrum differentiation, which create more possibilities to improve image quality. Multi-material decomposition is an attractive application for PCD-CT to identify complicated materials and provide accurate quantitative analysis. However, limited by the finite photon counting rate in each energy window of photon counting detector, the noise problem hinders the decomposition of high-quality basis material images. METHODS: To address this issue, an end-to-end multi-material decomposition network based on prior images is proposed in this paper. First, the reconstructed images corresponding to the full spectrum with less noise are introduced as prior information to improve the overall signal-to-noise ratio of the data. Then, a generative adversarial network is designed to mine the relationship between reconstructed images and basis material images based on the information interaction of material decomposition. Furthermore, a weighted edge loss is introduced to adapt to the structural differences of different basis material images. RESULTS: To verify the performance of the proposed method, simulation and real studies are carried out. In simulation study of structured fibro-glandular tissue model, the results show that the proposed method decreased the root mean square error by 67 % and 26 % on adipose, 66 % and 28 % on fibroglandular, 52 % and 8 % on calcification, compared to butterfly network and dual interactive Wasserstein generative adversarial network. CONCLUSION: Experimentally, the proposed method shows certain advantages over other methods on noise suppression effect, detail retention ability and decomposition accuracy.

Design and evaluation of mechanical strength of multi-material polymeric implants for mandibular reconstruction.

Reconstruction of mandible implants to address segmental abnormalities is still a challenging task, both in vitro and in vivo. The mechanical strength of the materials used is a critical factor that determines how well bone is regenerated. The reconstruction technique of mandibular abnormalities widely uses polymeric implants. It is critical to evaluate the mechanical resilience under different load cases, including axial, combined, and flexural loading conditions. This study developed implants for mandibular defects using a combination of four materials: polylactic acid (PLA), polyethylene terephthalate glycol (PETG), thermoplastic polyurethane (TPU), and polycaprolactone (PCL), with the aim of mimicking the inherent characteristics of cortical and cancellous bone structures and evaluating their mechanical properties to support bone Osseo integration. The eleven of these combinations of structures result below the micro strain threshold level of <3000 µÎµ, and the five combinations of the structures result in micro strain above the threshold value. The intact bone study results show that the stress under axial, combined, and flexural loading conditions is 27.6, 38.9, and 64.9 MPa, respectively. This study's stress results are lower than those from the intact bone study. The study found that the combinations of PLA and TPU material were most preferred for the cortical and cancellous bone regions of polymeric implants. These materials are also compatible with 3D printing. The results of this study can be used to find multi-material combinations that are strong and flexible.

An Image-Based Prior Knowledge-Free Approach for a Multi-Material Decomposition in Photon-Counting Computed Tomography.

Photon-counting CT systems generally allow for acquiring multiple spectral datasets and thus for decomposing CT images into multiple materials. We introduce a prior knowledge-free deterministic material decomposition approach for quantifying three material concentrations on a commercial photon-counting CT system based on a single CT scan. We acquired two phantom measurement series: one to calibrate and one to test the algorithm. For evaluation, we used an anthropomorphic abdominal phantom with inserts of either aqueous iodine solution, aqueous tungsten solution, or water. Material CT numbers were predicted based on a polynomial in the following parameters: Water-equivalent object diameter, object center-to-isocenter distance, voxel-to-isocenter distance, voxel-to-object center distance, and X-ray tube current. The material decomposition was performed as a generalized least-squares estimation. The algorithm provided material maps of iodine, tungsten, and water with average estimation errors of 4% in the contrast agent maps and 1% in the water map with respect to the material concentrations in the inserts. The contrast-to-noise ratio in the iodine and tungsten map was 36% and 16% compared to the noise-minimal threshold image. We were able to decompose four spectral images into iodine, tungsten, and water.

Numerical Simulation of the Evaporation Behavior of Fe-Mn Heterogeneous Powder in Selective Laser Melting Process.

Multi-material additive manufacturing using heterogeneous powders as raw materials is one of the important development directions of metal additive manufacturing technology. The evaporation behavior of heterogeneous powders in the selective laser melting (SLM) process has a significant influence on the accuracy of chemical composition control and the quality of the final product. In this paper, the fusion process of Fe20Mn (80 wt.% Fe and 20 wt.% Mn) heterogeneous powder, Fe and Mn elemental powders, and Fe20Mn pre-alloyed powder is numerically simulated using FLOW-3D® software and partially validated through SLM experimental results. The morphology and the characteristics of the flow field and temperature field in the melt pool for four kinds of powder materials are analyzed. The influence of the elemental evaporation behavior of different powders on the mass loss of the Mn element is discussed. The results show that the excessive accumulation of heat increases the maximum temperature of the melt pool, thus increasing mass loss. The Fe20Mn heterogeneous powder has a wider heat-affected zone and a higher peak value of temperature, nearly 400 K higher than that of the Fe20Mn pre-alloyed powders, which exhibits an intensive evaporation behavior. The mass loss of the Mn element obtained from the SLM experiment for Fe20Mn heterogeneous powders forming parts is more than the Fe20Mn pre-alloyed powders' forming parts for different laser powers, up to 17 wt.% at P = 120 W. This tendency is consistent with the numerical analysis of the effect of evaporation behavior of Fe-Mn heterogeneous powder during the SLM process. This study provides the necessary theoretical reference and process guidance for realizing the precise control of the SLM composition of a heterogeneous powder in multi-material additive manufacturing caused by evaporation behavior.

Three-Dimensional Printing of Ultrasoft Silicone with a Functional Stiffness Gradient.

A methodology for three-dimensionally printing ultrasoft silicone with a functional stiffness gradient is presented. Ultraviolet-cure silicone was deposited via two independently controlled extruders into a thixotropic, gel-like, silicone oil-based support matrix. Each extruder contained a different liquid silicone formulation. The extrusion rates were independently varied during printing such that the combined selectively deposited material contained different ratios of the two silicones, resulting in localized control of material stiffness. Tests to validate the process are reported, including tensile testing of homogeneous cubic specimens to quantify the range of material stiffness that could be printed, indentation testing of cuboid specimens to characterize printed stiffness gradients, and vibratory testing of synthetic multilayer vocal fold (VF) models to demonstrate that the method may be applied to the fabrication of biomechanical models for voice production research. The cubic specimens exhibited linear stress-strain data with tensile elasticity modulus values between 1.11 and 27.1 kPa, more than a factor of 20 in stiffness variation. The cuboid specimens exhibited material variations that were visually recognizable and quantifiable via indentation testing. The VF models withstood rigorous phonatory flow-induced vibration and exhibited vibratory characteristics comparable to those of previous models. Overall, while process refinements are needed, the results of these tests demonstrate the ability to print ultrasoft silicone with stiffness gradients.

Design of dissimilar material joint for defect-free multi-material additive manufacturing via laser-directed energy deposition.

Additive manufacturing technology has advanced beyond creating optimized features, from strengthening materials to make them lightweight to fabricating multi-material combinations that offer functionalities beyond the capabilities of individual materials. In this study, a lamination method for laser-directed energy deposition (LDED) is developed to achieve dense multi-material features, and a design that combines different and dissimilar materials is developed. To evaluate these novel developments, two materials-AISI 316L stainless steel and Inconel 625-are introduced. Tensile specimens, fabricated via multi-material additive manufacturing using LDED, are subjected to tensile tests that are recorded on video for digital image correlation. After the tests, fracture surface analyses of the fractured specimens are performed via scanning electron microscopy, and optical monitoring analyses are performed on the specimens that are not subjected to the tensile tests. The results indicate that the specimens demonstrate varied mechanical properties due to the influence of lamination direction and order, which affect the formation of critical cracks and pores.

Sheet-based extrusion bioprinting: a new multi-material paradigm providing mid-extrusion micropatterning control for microvascular applications.

As bioprinting advances into clinical relevance with patient-specific tissue and organ constructs, it must be capable of multi-material fabrication at high resolutions to accurately mimick the complex tissue structures found in the body. One of the most fundamental structures to regenerative medicine is microvasculature. Its continuous hierarchical branching vessel networks bridge surgically manipulatable arteries (∼1-6 mm) to capillary beds (∼10µm). Microvascular perfusion must be established quickly for autologous, allogeneic, or tissue engineered grafts to survive implantation and heal in place. However, traditional syringe-based bioprinting techniques have struggled to produce perfusable constructs with hierarchical branching at the resolution of the arterioles (∼100-10µm) found in microvascular tissues. This study introduces the novel CEVIC bioprinting device (i.e.ContinuouslyExtrudedVariableInternalChanneling), a multi-material technology that breaks the current extrusion-based bioprinting paradigm of pushing cell-laden hydrogels through a nozzle as filaments, instead, in the version explored here, extruding thin, wide cell-laden hydrogel sheets. The CEVIC device adapts the chaotic printing approach to control the width and number of microchannels within the construct as it is extruded (i.e. on-the-fly). Utilizing novel flow valve designs, this strategy can produce continuous gradients varying geometry and materials across the construct and hierarchical branching channels with average widths ranging from 621.5 ± 42.92%µm to 11.67 ± 14.99%µm, respectively, encompassing the resolution range of microvascular vessels. These constructs can also include fugitive/sacrificial ink that vacates to leave demonstrably perfusable channels. In a proof-of-concept experiment, a co-culture of two microvascular cell types, endothelial cells and pericytes, sustained over 90% viability throughout 1 week in microchannels within CEVIC-produced gelatin methacryloyl-sodium alginate hydrogel constructs. These results justify further exploration of generating CEVIC-bioprinted microvasculature, such as pre-culturing and implantation studies.

Recent Advances in 4D Printing of Advanced Materials and Structures for Functional Applications.

4D printing has attracted tremendous worldwide attention during the past decade. This technology enables the shape, property, or functionality of printed structures to change with time in response to diverse external stimuli, making the original static structures alive. The revolutionary 4D-printing technology offers remarkable benefits in controlling geometric and functional reconfiguration, thereby showcasing immense potential across diverse fields, including biomedical engineering, electronics, robotics, and photonics. Here, a comprehensive review of the latest achievements in 4D printing using various types of materials and different additive manufacturing techniques is presented. The state-of-the-art strategies implemented in harnessing various 4D-printed structures are highlighted, which involve materials design, stimuli, functionalities, and applications. The machine learning approach explored for 4D printing is also discussed. Finally, the perspectives on the current challenges and future trends toward further development in 4D printing are summarized.

Data of compressible multi-material flow simulations utilizing an efficient bimaterial Riemann problem solver.

This paper presents fluid dynamics simulation data associated with two test cases in the related research article [1]. In this article, an efficient bimaterial Riemann problem solver is proposed to accelerate multi-material flow simulations that involve complex thermodynamic equations of state and strong discontinuities across material interfaces. The first test case is a one-dimensional benchmark problem, featuring large density jump (4 orders of magnitude) and drastically different thermodynamics relations across a material interface. The second test case simulates the nucleation of a pear-shaped vapor bubble induced by long-pulsed laser in water. This multiphysics simulation combines laser radiation, phase transition (vaporization), non-spherical bubble expansion, and the emission of acoustic and shock waves. Both test cases are performed using the M2C solver, which solves the three-dimensional Eulerian Navier-Stokes equations, utilizing the accelerated bimaterial Riemann solver. Source codes provided in this paper include the M2C solver and a standalone version of the accelerated Riemann problem solver. These source codes serve as references for researchers seeking to implement the acceleration algorithms introduced in the related research article. Simulation data provided include fluid pressure, velocity, density, laser radiance and bubble dynamics. The input files and the workflow to perform the simulations are also provided. These files, together with the source codes, allow researchers to replicate the simulation results presented in the research article, which can be a starting point for new research in laser-induced cavitation, bubble dynamics, and multiphase flow in general.

Feasibility of bone marrow edema detection using dual-energy cone-beam computed tomography.

BACKGROUND: Dual-energy (DE) detection of bone marrow edema (BME) would be a valuable new diagnostic capability for the emerging orthopedic cone-beam computed tomography (CBCT) systems. However, this imaging task is inherently challenging because of the narrow energy separation between water (edematous fluid) and fat (health yellow marrow), requiring precise artifact correction and dedicated material decomposition approaches. PURPOSE: We investigate the feasibility of BME assessment using kV-switching DE CBCT with a comprehensive CBCT artifact correction framework and a two-stage projection- and image-domain three-material decomposition algorithm. METHODS: DE CBCT projections of quantitative BME phantoms (water containers 100-165 mm in size with inserts presenting various degrees of edema) and an animal cadaver model of BME were acquired on a CBCT test bench emulating the standard wrist imaging configuration of a Multitom Rax twin robotic x-ray system. The slow kV-switching scan protocol involved a 60 kV low energy (LE) beam and a 120 kV high energy (HE) beam switched every 0.5° over a 200° angular span. The DE CBCT data preprocessing and artifact correction framework consisted of (i) projection interpolation onto matched LE and HE projections views, (ii) lag and glare deconvolutions, and (iii) efficient Monte Carlo (MC)-based scatter correction. Virtual non-calcium (VNCa) images for BME detection were then generated by projection-domain decomposition into an Aluminium (Al) and polyethylene basis set (to remove beam hardening) followed by three-material image-domain decomposition into water, Ca, and fat. Feasibility of BME detection was quantified in terms of VNCa image contrast and receiver operating characteristic (ROC) curves. Robustness to object size, position in the field of view (FOV) and beam collimation (varied 20-160 mm) was investigated. RESULTS: The MC-based scatter correction delivered > 69% reduction of cupping artifacts for moderate to wide collimations (> 80 mm beam width), which was essential to achieve accurate DE material decomposition. In a forearm-sized object, a 20% increase in water concentration (edema) of a trabecular bone-mimicking mixture presented as ∼15 HU VNCa contrast using 80-160 mm beam collimations. The variability with respect to object position in the FOV was modest (< 15% coefficient of variation). The areas under the ROC curve were > 0.9. A femur-sized object presented a somewhat more challenging task, resulting in increased sensitivity to object positioning at 160 mm collimation. In animal cadaver specimens, areas of VNCa enhancement consistent with BME were observed in DE CBCT images in regions of MRI-confirmed edema. CONCLUSION: Our results indicate that the proposed artifact correction and material decomposition pipeline can overcome the challenges of scatter and limited spectral separation to achieve relatively accurate and sensitive BME detection in DE CBCT. This study provides an important baseline for clinical translation of musculoskeletal DE CBCT to quantitative, point-of-care bone health assessment.

Liver fat volume fraction measurements based on multi-material decomposition algorithm in patients with nonalcoholic fatty liver disease: the influences of blood vessel, location, and iodine contrast.

BACKGROUND: In recent years, spectral CT-derived liver fat quantification method named multi-material decomposition (MMD) is playing an increasingly important role as an imaging biomarker of hepatic steatosis. However, there are various measurement ways with various results among different researches, and the impact of measurement methods on the research results is unknown. The aim of this study is to evaluate the reproducibility of liver fat volume fraction (FVF) using MMD algorithm in nonalcoholic fatty liver disease (NAFLD) patients when taking blood vessel, location, and iodine contrast into account during measurement. METHODS: This retrospective study was approved by the institutional ethics committee, and the requirement for informed consent was waived because of the retrospective nature of the study. 101 patients with NAFLD were enrolled in this study. Participants underwent non-contrast phase (NCP) and two-phase enhanced CT scanning (late arterial phase (LAP) and portal vein phase (PVP)) with spectral mode. Regions of interest (ROIs) were placed at right posterior lobe (RPL), right anterior lobe (RAL) and left lateral lobe (LLL) to obtain FVF values on liver fat images without and with the reference of enhanced CT images. The differences of FVF values measured under different conditions (ROI locations, with/without enhancement reference, NCP and enhanced phases) were compared. Friedman test was used to compare FVF values among three phases for each lobe, while the consistency of FVF values was assessed between each two phases using Bland-Altman analysis. RESULTS: Significant difference was found between FVF values obtained without and with the reference of enhanced CT images. There was no significant difference about FVF values obtained from NCP images under the reference of enhanced CT images between any two lobes or among three lobes. The FVF value increased after the contrast injection, and there were significant differences in the FVF values among three scanning phases. Poor consistencies of FVF values between each two phases were found in each lobe by Bland-Altman analysis. CONCLUSION: MMD algorithm quantifying hepatic fat was reproducible among different lobes, while was influenced by blood vessel and iodine contrast.

Multi-material 3D printed eutectogel microneedle patches integrated with fast customization and tunable drug delivery.

Microneedle patches are emerging multifunctional platforms for transdermal diagnostics and drug delivery. However, it still remains challenging to develop smart microneedles integrated with customization, sensing, detection and drug delivery by 3D printing strategy. Here, we present an innovative but facile strategy to rationally design and fabricate multifunctional eutectogel microneedle (EMN) patches via multi-material 3D printing. Polymerizable deep eutectic solvents (PDES) were selected as printing inks for rapid one-step fabrication of 3D printing functional EMN patches due to fast photopolymerization rate and ultrahigh drug solubility. Moreover, stretchable EMN patches incorporating rigid needles and flexible backing layers were easily realized by changing PDES compositions of multi-material 3D printing. Meanwhile, we developed multifunctional smart multi-material EMN patches capable of performing wireless monitoring of body movements, painless colorimetric glucose detection, and controlled transdermal drug delivery. Thus, such multi-material EMN system could provide an effective platform for the painless diagnosis, detection, and therapy of a variety of diseases.

Low-Temperature Extrusion of Waterborne Polyurethane-Polycaprolactone Composites for Multi-Material Bioprinting of Engineered Elastic Cartilage.

3D bioprinting of elastic cartilage tissues that are mechanically and structurally comparable to their native counterparts, while exhibiting favorable cellular behavior, is an unmet challenge. A practical solution for this problem is the multi-material bioprinting of thermoplastic polymers and cell-laden hydrogels using multiple nozzles. However, the processing of thermoplastic polymers requires high temperatures, which can damage hydrogel-encapsulated cells. In this study, the authors developed waterborne polyurethane (WPU)-polycaprolactone (PCL) composites to allow multi-material co-printing with cell-laden gelatin methacryloyl (GelMA) hydrogels. These composites can be extruded at low temperatures (50-60 °C) and high speeds, thereby reducing heat/shear damage to the printed hydrogel-capsulated cells. Furthermore, their hydrophilic nature improved the cell behavior in vitro. More importantly, the bioprinted structures exhibited good stiffness and viscoelasticity compared to native elastic cartilage. In summary, this study demonstrated low-temperature multi-material bioprinting of WPU-PCL-based constructs with good mechanical properties, degradation time-frames, and cell viability, showcasing their potential in elastic cartilage bio-fabrication and regeneration to serve broad biomedical applications in the future.

Novel strategy for multi-material 3D bioprinting of human stem cell based corneal stroma with heterogenous design.

Three-dimensional (3D) bioprinting offers an automated, customizable solution to manufacture highly detailed 3D tissue constructs and holds great promise for regenerative medicine to solve the severe global shortage of donor tissues and organs. However, uni-material 3D bioprinting is not sufficient for manufacturing heterogenous 3D constructs with native-like microstructures and thus, innovative multi-material solutions are required. Here, we developed a novel multi-material 3D bioprinting strategy for bioprinting human corneal stroma. The human cornea is the transparent outer layer of your eye, and vision loss due to corneal blindness has serious effects on the quality of life of individuals. One of the main reasons for corneal blindness is the damage in the detailed organization of the corneal stroma where collagen fibrils are arranged in layers perpendicular to each other and the corneal stromal cells grow along the fibrils. Donor corneas for treating corneal blindness are scarce, and the current tissue engineering (TE) technologies cannot produce artificial corneas with the complex microstructure of native corneal stroma. To address this, we developed a novel multi-material 3D bioprinting strategy to mimic detailed organization of corneal stroma. These multi-material 3D structures with heterogenous design were bioprinted by using human adipose tissue -derived stem cells (hASCs) and hyaluronic acid (HA) -based bioinks with varying stiffnesses. In our novel design of 3D models, acellular stiffer HA-bioink and cell-laden softer HA-bioink were printed in alternating filaments, and the filaments were printed perpendicularly in alternating layers. The multi-material bioprinting strategy was applied for the first time in corneal stroma 3D bioprinting to mimic the native microstructure. As a result, the soft bioink promoted cellular growth and tissue formation of hASCs in the multi-material 3D bioprinted composites, whereas the stiff bioink provided mechanical support as well as guidance of cellular organization upon culture. Interestingly, cellular growth and tissue formation altered the mechanical properties of the bioprinted composite constructs significantly. Importantly, the bioprinted composite structures showed good integration to the host tissue in ex vivo cornea organ culture model. As a conclusion, the developed multi-material bioprinting strategy provides great potential as a biofabrication solution for manufacturing organized, heterogenous microstructures of native tissues. To the best of our knowledge, this multi-material bioprinting strategy has never been applied in corneal bioprinting. Therefore, our work advances the technological achievements in additive manufacturing and brings the field of corneal TE to a new level.

Light-Based 3D Multi-Material Printing of Micro-Structured Bio-Shaped, Conducting and Dry Adhesive Electrodes for Bioelectronics.

In this work, a new method of multi-material printing in one-go using a commercially available 3D printer is presented. The approach is simple and versatile, allowing the manufacturing of multi-material layered or multi-material printing in the same layer. To the best of the knowledge, it is the first time that 3D printed Poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) micro-patterns combining different materials are reported, overcoming mechanical stability issues. Moreover, the conducting ink is engineered to obtain stable in-time materials while retaining sub-100 µm resolution. Micro-structured bio-shaped protuberances are designed and 3D printed as electrodes for electrophysiology. Moreover, these microstructures are combined with polymerizable deep eutectic solvents (polyDES) as functional additives, gaining adhesion and ionic conductivity. As a result of the novel electrodes, low skin impedance values showed suitable performance for electromyography recording on the forearm. Finally, this concluded that the use of polyDES conferred stability over time, allowing the usability of the electrode 90 days after fabrication without losing its performance. All in all, this demonstrated a very easy-to-make procedure that allows printing PEDOT:PSS on soft, hard, and/or flexible functional substrates, opening up a new paradigm in the manufacturing of conducting multi-functional materials for the field of bioelectronics and wearables.

Multi-energy CT material decomposition using graph model improved CNN.

In spectral CT imaging, the coefficient image of the basis material obtained by the material decomposition technique can estimate the tissue composition, and its accuracy directly affects the disease diagnosis. Although the precision of material decomposition is increased by employing convolutional neural networks (CNN), extracting the non-local features from the CT image is restricted using the traditional CNN convolution operator. A graph model built by multi-scale non-local self-similar patterns is introduced into multi-material decomposition (MMD). We proposed a novel MMD method based on graph edge-conditioned convolution U-net (GECCU-net) to enhance material image quality. The GECCU-net focuses on developing a multi-scale encoder. At the network coding stage, three paths are applied to capture comprehensive image features. The local and non-local feature aggregation (LNFA) blocks are designed to integrate the local and non-local features from different paths. The graph edge-conditioned convolution based on non-Euclidean space excavates the non-local features. A hybrid loss function is defined to accommodate multi-scale input images and avoid over-smoothing of results. The proposed network is compared quantitatively with base CNN models on the simulated and real datasets. The material images generated by GECCU-net have less noise and artifacts while retaining more information on tissue. The Structural SIMilarity (SSIM) of the obtained abdomen and chest water maps reaches 0.9976 and 0.9990, respectively, and the RMSE reduces to 0.1218 and 0.4903 g/cm3. The proposed method can improve MMD performance and has potential applications.

Diffusion-Based 3D Bioprinting Strategies.

3D bioprinting has enabled the fabrication of tissue-mimetic constructs with freeform designs that include living cells. In the development of new bioprinting techniques, the controlled use of diffusion has become an emerging strategy to tailor the properties and geometry of printed constructs. Specifically, the diffusion of molecules with specialized functions, including crosslinkers, catalysts, growth factors, or viscosity-modulating agents, across the interface of printed constructs will directly affect material properties such as microstructure, stiffness, and biochemistry, all of which can impact cell phenotype. For example, diffusion-induced gelation is employed to generate constructs with multiple materials, dynamic mechanical properties, and perfusable geometries. In general, these diffusion-based bioprinting strategies can be categorized into those based on inward diffusion (i.e., into the printed ink from the surrounding air, solution, or support bath), outward diffusion (i.e., from the printed ink into the surroundings), or diffusion within the printed construct (i.e., from one zone to another). This review provides an overview of recent advances in diffusion-based bioprinting strategies, discusses emerging methods to characterize and predict diffusion in bioprinting, and highlights promising next steps in applying diffusion-based strategies to overcome current limitations in biofabrication.