Additive manufacturing (AM) is increasingly being explored not only for prototyping and model fabrication, but also for producing end-use components. Among various AM techniques, material extrusion is widely adopted due to its accessibility and versatility; however, a limitation of this approach is anisotropic mechanical properties - in particular reduced strength in the interlayer direction. This study explores the application of atmospheric cold plasma treatment to enhance the wettability and mechanical performance of Fused Filament Fabrication (FFF) 3D printed parts. By integrating a plasma treatment wand within a 3D printer, the effects of in-situ plasma treatment are explored on two polymers: polylactic acid (PLA), and thermoplastic polyurethane (TPU). Plasma-treated samples exhibit significantly improved surface wettability, with water contact angles decreasing by more than 50

This study reports CO2-derived carbon nanofiber (CCNF) as a sustainable filler for Polyvinylidene fluoride (PVDF)- based conductive composites prepared by melt mixing and solvent casting. Using CCNF alongside conventional carbon black and carbon nanotubes, we quantify how the processing technique and the hightemperature treatment affect the percolation behavior and electrical conductivity of CCNF reinforced composites. Compared to melt mixing, solvent casting with ultrasonication yields markedly better dispersion and earlier network formation than melt mixing, delivering higher DC conductivity at comparable loadings. The thermally treated CCNFs (CCNF900) further improved the performance, with the filler's intrinsic electrical conductivity increasing threefold and the percolation threshold decreasing by 46%. Fourier transform infrared (FTIR) and Xray diffraction (XRD) analyses reveal that CCNF900 indicate improved interfacial compatibility while preserving the pre-existing beta-phase fraction, enabling conductive networks without excessive phase disruption. Overall, CCNF-based PVDF composites demonstrate clear advantages in improving electrical conductivity, shifting percolation to lower loadings, and facilitating sustainable processing, highlighting CCNF as a promising sustainable for scalable conductive structures.

Aerogels are a unique class of ultralight porous materials distinguished by their high porosity, low heat conductivity, mechanical flexibility, and large surface area. These qualities make them attractive for a variety of uses, such as biomedical devices, energy storage, thermal insulation, environmental remediation, and catalysis. Despite this potential, the complex gelation chemistry and sensitivity to processing conditions make conventional aerogel synthesis and optimization labor-intensive, expensive, and challenging to scale. Through data-driven design, accelerated property prediction, and process optimization, machine learning (ML) presents compelling opportunities to address these issues. This review offers a thorough analysis of recent developments at the nexus of aerogel research and ML, focusing on the identification of structure–property relationships, the prediction of mechanical, thermal, and physical properties, and design optimization, including inverse design techniques. We offer an integrated viewpoint that connects computational simulations, experimental workflows, and ML techniques, showcasing developments like autonomous materials development, physics-guided learning, and generative models. Data collection, preprocessing, feature engineering, dimensionality reduction, and suitable algorithm selection are all covered in a generalized ML pipeline for aerogels. Lastly, we provide a roadmap for next-generation ML-enabled aerogel materials by examining future directions such as fabrication optimization, data augmentation, automated processing, hybrid modelling, inverse design, and ML-enhanced characterization.

Epidermal electrodes are essential for wearable bioelectronics in health monitoring and rehabilitation. However, current options are limited: adhesives irritate skin, hydrogels dry out, ultrathin films cannot deliver high currents, and sharp microstructures concentrate current, risking burns. Achieving repeatable gel-mediated adhesion with uniform current distribution on curved, moving skin remains a major challenge. In this study, we present biomimetic electrodes using carbon nanotube-silicone elastomer (CNT-elastomer) composites patterned with octopus-inspired suction units and polyethylenimine (PEIE)-induced microwrinkles. With an interfacing electrolyte gel, these electrodes achieve strong, repeatable skin adhesion of up to 77 ± 5 J m-2 in the normal direction and maintain performance over 100 reapplication cycles. Charge-transfer resistance across the skin is reduced by 20-fold, establishing low-impedance interfaces. The electrodes record electrophysiological signals with clinical-grade fidelity and deliver surface functional electrical stimulation with 50% higher efficiency in large muscle groups. Manufactured through facile fabrication methods of mold casting and leaving no skin residue, the electrodes exhibit skin-matching mechanical compliance and efficient charge transfer, advancing comfortable and durable bioelectronic interfaces for rehabilitation, assistive neurotechnology, and next-generation healthcare devices.

Background Stroke remains a leading cause of global disability, and current clinical treatments offer limited options for neural repair. Adult endogenous neural precursor cells are electrosensitive and can be activated by externally applied electric fields, a property that can be harnessed to augment existing stem cell-based therapies. In this study, we investigated whether clinically relevant charge-balanced biphasic monopolar electric fields could promote functional recovery and tissue regeneration in a mouse model of stroke. Methods Transgenic Sox2-CreER T2 ::tdTomato mice were utilized to pre-label and lineage trace neural precursor cell population. Animals received wireless intermittent cortical biphasic monopolar electrical fields stimulation (3 hours/day for 3 days) under baseline uninjured conditions or following Endoethelin-1 induced ischemic stroke. Cellular and tissue-level outcomes were evaluated using behavioural assays, clonally derived colony neurosphere assays, and immunohistochemical analyses to quantify neural stem cell pool expansion, directed cathodal migration, differentiation, neuronal survival, and microglial activation. Furthermore, to establish a direct causal mechanism, Sox2 TK mice underwent targeted neural precursor cell ablation during the subacute phase of stroke. Between-group differences were evaluated using comparative statistical analyses. Results In healthy non-injured mice, wireless intermittent cortical stimulation successfully activated adult endogenous neural precursor cells, driving expanding the stem cell pool, cathodal migration, and differentiation, without exacerbating baseline neuroinflammation or altering normal animal behaviour. Following ischemic stroke, the 3-day electrical stimulation paradigm significantly improved motor recovery, enhanced peri-infarct neuronal survival, and reduced microglial activation. Importantly, mice subjected to neural precursor cell ablation post-stroke exhibited neither functional behavioural recovery nor a reduction in neuroinflammatory response despite receiving electrical stimulation. Conclusions Endogenous neural precursor cell activation serves as the primary cellular mechanism underlying biphasic monopolar electrical stimulation-mediated neural repair and functional recovery in the stroke-injured brain. These findings demonstrate the therapeutic potential of wireless stimulation to drive targeted neural stem cell activation that promotes sustained functional recovery after focal ischemic stroke.

Lithium-ion batteries (LIBs) continue to underpin modern energy-storage technologies, yet their performance is limited by sluggish ion transport, dendrite formation, and structural degradation during cycling. Here, we introduce a binder-free hybrid electrode composed of Ti3C2Tx MXene and reduced graphene oxide nanoribbons (rGOnRs), engineered through a directional freeze-casting process to create a porous, mechanically robust 3D architecture. This microstructured network prevents MXene restacking, lowers tortuosity for rapid ion transport, and enables uniform Li-ion distribution during cycling. Subsequent chemical and thermal reductions convert GOnRs into highly conductive graphene nanoribbons (GnRs) while simultaneously modifying MXene terminal groups, forming conductive heterostructures that enhance electronic coupling and provide additional active sites for reversible Li+ storage. The Ti3C2Tx/GnR electrode delivers a discharge capacity of 401 mAh/g at 0.2 A/g, corresponding to a Coulombic efficiency of similar to 97%. After 200 cycles, the electrode retains 92% of its initial capacity, demonstrating excellent cycling stability. Even at 5 A/g, the electrode maintains a high capacity of 370 mAh/g, demonstrating exceptional rate capability and reversibility. Density functional theory (DFT) calculations further reveal favorable charge transfer and lowered Li+ migration barriers at the Ti3C2Tx/GnR interface, supporting the experimentally observed enhancements. Together, the synergistic interplay between microstructuring, terminal-group engineering, and heterostructure formation establishes a scalable and commercially viable pathway for high-performance LIB anodes.

Recent advances in machine learning have seen a wide range of applications across many fields. When combined with developments in flexible, skin-interfaced pressure sensors, these technologies are driving a new generation of personalized health monitoring. From preventing diabetic foot ulcers to tracking respiratory rate and other vital signs, these systems are advancing smarter and more responsive healthcare solutions. This review presents a comprehensive overview of the latest developments in skin-interfaced flexible pressure sensing, starting from their physical mechanisms and ending at microscale material structure. In addition, advanced machine learning approaches for sensor data processing and interpretation is explored, ranging from the fundamental concepts to more recent deep learning models such as temporal convolutional networks for time series classification. Moreover, a systematic review of recent literature is presented, highlighting the application of machine learning in analyzing signals from flexible pressure sensors. Emerging applications leveraging machine learning techniques to facilitate smart health monitoring and human-machine interfaces are explored. A concluding section outlines the challenges and outlook for these emerging technologies as it relates to the biomedical field. To the best of our knowledge, this is the first review which evaluates the potential integration between skin-interfaced flexible pressure sensors with cutting-edge machine learning models, offering a synergistic perspective on next-generation biomedical applications.

This work investigates the adhesion dynamics and interfacial behavior of ethylene vinyl alcohol (EVOH) and a commercial tie layer based on polyethylene-grafted maleic anhydride (PE-g-MA) films, manufactured through a multilayer co-extrusion process. The interfacial morphology is significantly influenced by the co-extrusion parameters and the cooling conditions. Moreover, increasing the co-extrusion temperature leads to an increase in interface thickness and induces greater entanglement, thereby enhancing adhesion. Additionally, water quenching caused the interdiffusion of the adjacent polymers across the interface to freeze and, in turn, resulted in a strong adhesion between EVOH and the tie layer. The corresponding failure mode is predominantly cohesive, particularly at elevated temperatures (60 degrees C and 80 degrees C), where we observed a 250 % improvement in the peel strength. Conversely, air cooling (slow) yielded adhesive failures irrespective of the peel test temperature. This study demonstrates the importance of understanding the process and the interfacial behavior that drives the adhesion mechanism between EVOH and PE-g-MA at room and high temperatures.

Aerogels are a unique class of nanostructured materials renowned for their exceptional properties, including ultralow density, outstanding thermal insulation, and low dielectric constant. Traditionally, aerogels have been fabricated through sol-gel processing in various geometries; however, the emergence of additive manufacturing (AM) has revolutionized their processing by introducing unprecedented design flexibility. AM offers significant advantages such as customized geometries, tailored structures, and enhanced scalability for practical and industrial applications. This review provides an in-depth analysis of recent advancements in the AM of aerogels, beginning with a discussion on the fundamentals of aerogel materials, including their sol-gel processing, drying techniques, and formability. Both organic and inorganic aerogels are explored, emphasizing their unique properties and potential applications. The review then delves into the concept of AM, categorizing its various approaches, and examines cutting-edge AM techniques for aerogel fabrication, such as direct ink writing (DIW), direct cryo writing (DCW), inkjet printing, and vat polymerization (VP). Furthermore, post-processing strategies to enhance the structural and functional properties of additively manufactured aerogels are discussed, highlighting their transformative potential across diverse fields.

Multilayer thermoplastic composites offer sustainable alternatives to traditional thermoset and metal materials. However, their design is inherently complex, involving numerous interdependent parameters that render conventional processes both expensive and time-consuming. While machine learning-assisted methods provide a potential solution, they typically require large datasets that can be costly to obtain. This study explores a robust neural network, specifically, an Advanced Multilayer Perceptron (AdvMLP) Regressor, to predict the peel strength of multilayer thermoplastic composites. Through architectural enhancements, the AdvMLP is effectively trained on a limited yet authentic manufacturing dataset, yielding robust predictions validated by benchmark metrics and k-fold cross-validation. The model captures the intricate interplay between manufacturing processes and composite properties, enabling comprehensive feature importance analysis and dimensionality reduction. Overall, this study establishes a robust and generalizable machine learning-assisted methodology to guide and accelerate the design and optimization of multilayer thermoplastic composites.

Epoxies used in optoelectronic applications are prone to creep due to low-magnitude stresses caused by uneven thermal expansion, residual stresses, or component weight in elevated temperature environments. To address this, the effect of adding graphene nanoplatelets (GNP, 0.1-1 wt.%) and halloysite nanotubes (HNT, 1-10 wt.%) on the creep behavior of epoxy nanocomposites was investigated through 1-h creep tests at 60 degrees C under a 5 MPa load. Results showed that adding 0.1 wt.% GNP and 10 wt.% HNT led to significant reductions in strain rate, by 78.2% and 77.4%, respectively. A synergistic effect was observed when both nanoparticles were combined, improving dispersion and overall performance. The hybrid nanocomposite containing 1 wt.% HNT and 0.25 wt.% GNP demonstrated the most balanced improvement, with a 68.5% reduction in strain rate and enhanced dispersion. Characterization of the particle distribution using optical microscopy and SEM revealed that GNP particles tended to agglomerate more than HNT, while higher HNT loadings caused particle clustering and settling. The hybrid nanocomposite effectively mitigated these issues, making it a promising candidate for high-temperature, high-performance applications in optoelectronics, where improved mechanical and thermal stability are essential.Highlights 0.1 wt.% GNP and 10 wt.% HNT reduce the strain rate by 78.2% and 77.4%, respectively. Synergistic effect of 0.25 wt.% GNP and 1 wt.% HNT enhances epoxy creep resistance by 68.5%. GNP and HNT enhance creep strain by reinforcing the polymer network and load transfer. Optimized epoxy nanocomposite is ideal for high-temperature optoelectronic applications.

Contact electrification is the primary mechanism dictating electron transfer and surface charge density for triboelectric nanogenerators (TENGs), making intrinsic material and physical surface properties key parameters for the interfacial charge transfer phenomena. Surface properties are governed by the morphological and textural microstructural features, including tribological interactions, topographical profiling, surface roughness, and real contact area. Therefore, understanding surface morphological effects on the triboelectric performance aids development towards adapting and optimizing surface properties. Particularly, in polymer-based composites TENGs, the surface morphology relies on polymer crystallization and interactions with reinforcing additives. This comprehensive study evaluated the effects of isothermal crystallization and the incorporation and dispersibility of raw and few-layer exfoliated muscovite mica fillers, insightfully realizing and tuning polyethylene oxide's intrinsic properties and semi-crystalline microstructure. The full material characterization presented dramatic variations in polymer growth kinetics, chain dynamics, lamellae profiling, surface roughness, and work functions, allowing the development of a constructive triboelectric surface microstructural design guide. The crystallization temperature of 65 degrees C with raw mica demonstrated the greatest dielectric properties and triboelectric performance resulting in a peak-to-peak voltage, peak-to-peak current density, transferred charge density, and power density of respectively, 488 V, 45.5 mA m-2, 152 mu C m-2, and 24.0 W m-2 at a load resistance of 6 M Omega. The TENG device demonstrated stable long-term voltage outputs over the duration of 12 000 contact-separation cycles and successfully self-powered natural resource environmental monitoring sensors.

Improving the fiber-matrix adhesion in thermoplastic composites remains a significant challenge due to the lack of chemical bonding between thermoplastics and common reinforcing fibers. This study investigates the effectiveness of carbon fibers enhanced with nanostructure surface modification for strengthening the interfacial adhesion to thermoplastic matrices. The fiber surface was modified with graphene nanoplatelets (GNP) through a facile coating method, and the apparent interfacial shear strength (IFSS) was determined by single-fiber pullout tests. GNP-coated fiber improved IFSS by 74 % with neat high-density polyethylene (HDPE-Neat) and 28 % with maleic anhydride-grafted HDPE (HDPE-8MA), while IFSS reduced by 27 % with polyamide 6 (PA6) due to different failure mechanisms. Morphology, chemical, and wettability analysis were conducted on the nanoenhanced carbon fibers to quantitatively elucidate these findings on micro/nanoscale, combining machine learning-based image segmentation, X-ray photoelectron spectroscopy (XPS), and contact-angle measurements of intermittent beading on fibers.

Introduction: Functional electrical stimulation (FES) is a common neurorehabilitation technique that uses electrodes to apply electrical current to muscles to generate a functional movement often used as a therapy for those with limited motor control. Complex neuroprosthetic systems have been developed to regulate balance in individuals with spinal cord injury (SCI) through closed-loop systems that apply FES to the lower limbs and trunk in response to the user moving towards an unstable state. Since these systems are designed to perform FES on various muscle groups simultaneously, many electrodes are required to be placed during therapy. This is a cumbersome process that takes a significant amount of time, and it is not able to be done easily by those with limited dexterity. Textile-based electrodes attempt to address this issue by allowing the user to place many electrodes simultaneously by having multiple electrodes pre-positioned in one piece of clothing. However, most of these textile-based electrodes are required to be used wet to deliver comfortable stimulation, and their ability to withstand laundering has not been adequately evaluated. Moreover, existing textile-based electrode designs do not account for the accessibility needs of the end-users. Garments with textile-embedded electrodes often take the form of compression clothing to ensure adequate electrode-skin contact, making it difficult to use by those with SCI due to the tightness of the garment. Furthermore, the process for connecting each individual electrode to the stimulator must also be streamlined for potential end-users to utilize these systems with ease. Thus, we were motivated to develop a new electrode material and garment design to address these challenges which could perform equivalently to existing options. Methods: By combining CNT and a polymer, we were able to manufacture thin film electrodes that can be permanently adhered to a garment using a commercial heat press. We characterized the mechanical and electrical properties of this novel electrode material and compared it to similar dry electrode materials and standard hydrogel electrodes. Experiments are currently ongoing to verify the performance of these new electrodes during stimulation compared to standard self-adhesive hydrogel electrodes with able-bodied participants, as well as to verify their robustness following numerous washing cycles. Results: When compared to other dry electrode materials, the new electrode material was found to be very flexible and stretchable, making it better suited to conform to the skin to optimize the electrode-skin interface. Electrochemical impedance spectroscopy (EIS) analysis showed that the impedance of the new electrode material used in a garment was comparable to that of standard hydrogel electrodes. A prototype sleeve has been developed for the lower leg that is fully adjustable to accommodate different sized and shaped legs which can be easily tightened/loosened to enable ease of donning/doffing the garment. In addition, a connector has been designed to attach to the garment to connect many electrodes to the stimulator at once, while requiring limited force to attach or remove the connector from the garment. Discussion and conclusion: The development of a dry, flexible, and stretchable electrode material that can be easily integrated into garments addresses several limitations of current textile-based electrodes. The result of this prototype sleeve is a basis for which a full garment for the lower limbs and trunk can be developed for use with not only complex standing neuroprosthesis, but also any FES applications for the lower limbs. Figure 1 : Interior of garment showing dry electrode surface to be in contact with skin Figure 1

Harnessing the potential of endogenous neural precursor cells (NPCs) to enhance neural repair is a promising strategy with demonstrated success. The use of electrical stimulation and delivery of neurotrophic factors has shown great potential for activating NPCs. However, traditional implantable electrodes for brain stimulation involve interfaces that can cause an inflammatory response and neuronal cell death around the implant site, which imposes risks and costs related to surgical extraction after the treatment period. Here, we introduce a biodegradable, multi-modal penetrating electrode that activates NPCs. The design consists of microfabricated stimulation electrodes integrated with a microfluidic pharmaceutical delivery channel made of bioresorbable materials. The stimulation electrodes demonstrate superior electrochemical performance compared with conventional electrodes with respect to the delivery of optimized current and electric fields to the brain. The bioresorbable delivery system can deliver neurotrophic factors into mouse cortices without leakage, backflow, or backtracking. The entire design undergoes harmless degradation over time and disappears in physiological conditions. The development this platform enables the delivery of combinatorial approaches to activate NPCs and support neuroplasticity to enhance neural repair. The biodegradable electrode can also be used in applications involving temporary neuromodulation and/or stem cell-based therapies to promote neural repair.

Recent advancements in multifunctional materials have underscored the importance of nanostructured porous aerogels in the development of high-performance triboelectric nanogenerators (TENGs) for diverse applications. The unique nanostructured assembly of aerogels integrates a substantial volume of air within the material, leading to ultra-low density and enhanced electrical insulation properties. In this study, we employed polyvinylidene fluoride (PVDF) and graphene nanoplatelet (GnP) to develop an aerogel with superior triboelectric performance. The abundant dipoles within the PVDF matrix, coupled with the conductive GnP nanoparticles and their interaction with the polymer chains, significantly improved charge transfer throughout the aerogel’s skeletal framework. This enhancement was reflected in the aerogel’s triboelectric performance, where it effectively powered capacitors representative of low-power electronics, achieving a peak power density of 307.5mW/m2. By optimizing the polymer load and physical crosslinker via sol-gel processing, the mechanical cohesiveness and flexibility of the aerogel were enhanced. Leveraging its ultra-low density of 0.25g/cm3, enhanced triboelectric performance, and mechanical flexibility, a Sound Aerogel-based Sensor TENG (SAS-TENG) was designed. The sensor demonstrated that acoustic stimuli within the 40dB to 120dB range provided sufficient force to mobilize the ultra-lightweight aerogel, thereby activating the contact-and-separation mode TENG. The voltage output ranged from 1.05V to 4.85V as the acoustic stimuli increased from 40dB to 120dB. The SAS-TENG exhibited an impressive specific power output of 15.37mW/g, highlighting its potential for development of lightweight, self-powered sound sensors in IoT networks.

As the world shifts toward clean energy systems and electric transportation, the need for advanced, long-lasting lithium-ion batteries (LIBs) becomes increasingly urgent. Applications from grid-scale energy storage to rapidly charging electric vehicles place unprecedented demands on battery electrodes—requiring higher energy densities, faster ion transport, and robust stability over extended cycling. Among emerging electrode materials, two-dimensional (2D) transition metal carbides and nitrides (MXenes) show particular promise. Ti₃C₂Tₓ MXene, in particular, offers high electrical conductivity, tunable surface chemistry, and layered structures theoretically capable of accommodating abundant lithium-ion storage. However, real-world implementations rarely approach this theoretical potential due to persistent challenges such as sheet restacking, restricted electrolyte penetration, and interfacial instabilities. Additionally, the pervasive use of polymeric binders often impedes electron transfer, reduces accessible active sites, and contributes to long-term degradation. To tackle these issues, we introduce a comprehensive electrode engineering strategy that leverages the synergy between Ti₃C₂Tₓ MXene and graphene nanoribbons (GnRs). Instead of relying on conventional binders that insulate and obstruct ion and electron movement, we incorporate GnRs to mechanically stabilize the electrode and maintain electrically conductive pathways. This approach preserves the inherent advantages of the MXene layers—expansive surface area, high conductivity, and adjustable surface groups—while ensuring open channels for ion transport and electron conduction. By protecting active sites from blockage, we enhance the utilization of the MXene’s theoretical capacity. A key element of this framework is the use of freeze-casting to shape the MXene–GnR composite into a three-dimensional scaffold. During freezing, ice crystals impose a vertical order on the dispersed flakes and ribbons. After the ice is removed by sublimation, what remains is a porous, aerogel-like electrode with aligned channels. These ordered pores promote uniform Li-ion distribution, reduce tortuosity, and prevent the restacking of MXene flakes that can limit capacity. The resultant architecture not only enhances ion and electrolyte transport but also maintains mechanical integrity during repeated lithiation and delithiation cycles, mitigating the risk of structural collapse and improving long-term stability. This strategy also tackles the isiue of unwanted chemical reactions as we employ targeted post-processing treatments. Gentle thermal annealing and chemical reduction steps remove oxygen-containing groups from the GnRs, thereby improving their conductivity and ensuring stronger electronic coupling with the MXene flakes. Simultaneously, refining the MXene surface terminations reduces detrimental reactions with the electrolyte, stabilizing the electrode–electrolyte interface. These combined adjustments enable a more stable electrochemical environment, increasing the reversibility of lithium storage and preserving capacity over prolonged cycling. The electrochemical performance of our Ti₃C₂Tₓ@GnR electrode system validates the effectiveness of this integrated approach. Freed from insulating binders, the electrode demonstrates significantly enhanced capacity and rate capability. At a moderate current density of 0.2 A g⁻¹, the electrode achieves a specific capacity of 389 mAh g⁻¹ during charge and 401 mAh g⁻¹ during discharge. Even more impressively, it retains approximately 92% of its initial capacity over extended cycling, reflecting robust structural and interfacial stability. High-rate performance is equally encouraging: at a demanding current density of 5 A g⁻¹, the electrode still delivers a capacity of 220 mAh g⁻¹, showcasing rapid ionic transport and excellent reversibility under fast-charging conditions. These results mark a notable shift from incremental improvements to a more holistic re-envisioning of electrode design. By unifying material selection, structural engineering, and interfacial tuning, we harmonize multiple performance attributes—capacity, rate capability, and long-term stability—into a single, cohesive solution. Beyond just laboratory demonstrations, this approach aligns with industrial considerations. The freeze-casting process is solution-based, scalable, and does not necessitate complex or vacuum-intensive steps. The absence of polymer binders, coupled with the widespread availability of carbon nanostructures, reduces barriers to mass production, making this concept commercially appealing. More broadly, the principles outlined here—integrating 2D materials with tailored additives, employing ice-templating to achieve an ideal porous architecture, and using post-treatments to fine-tune chemistry—hold potential for a variety of next-generation electrode systems.

Material extrusion 3D printing is an up-and-coming additive manufacturing method that is continuously being explored for its many benefits including rapid prototyping, high degree of customizability, and low material waste production, among others. One of the most widely used materials in material extrusion 3D printing is polylactic acid (PLA) due to its ease of printability and bio-origins. Recently, new biofiller reinforced PLA biocomposite filaments have begun being sold commercially, but the introduction of the biofiller creates problems of increased hydrophilicity and hygroscopicity. In this study, a possible solution to this problem was explored by using multimaterial 3D printing to add a thin, structured outer layer to the biocomposite in either pure PLA or TPU. This layer helps limit any external moisture from coming into contact with the underlying biocomposite by creating a barrier with increased hydrophobicity. A grid, triangle, and honeycomb pattern were each tested at 50 %, 75 %, and 100 % pattern densities for each material. It was found that, along with the pattern that was printed, the filament deposition process created additional roughness that influenced the way the water droplets interacted with the surface. All the patterned surfaces displayed a higher water contact angle than when the material was printed in a flat manner. Additionally, factors that influence the feasibility of using this outer structured layer to improve the surface hydrophobicity of biocomposite parts were explored, including material compatibility and adhesion.