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Curl distortion has been a persistent challenge for vat photopolymerization‐based printing technology such as digital light processing (DLP), leading to structural deformation and print failures. This study presents a new approach to mitigate curling distortion and heat effects during DLP printing by dividing the printing layer image into sequential subimages, using a breadth‐first search algorithm. The progressive curing process, resembling a ripple pattern, results in a significant improvement in printing accuracy. The deviation is reduced tenfold when the layer image is divided into subimages with 10 pixels for a 32 mm diameter disc. Additionally, subdivision strategy helps to reduce the heat effect during photopolymerization, as monitored in situ by a long‐wave infrared camera. The successful reduction of residual stress using the subdivision strategy results in a 75% improvement in the mechanical performance of the printed products. The simple adoption of subdivision strategy in practical 3D printing applications is also demonstrated. For solid 3D printing structures, introducing intervals within the solid printing layers—such as using a grid structure instead of a fully solid one, can help to reduce curling and heat effects, thereby improving 3D printing accuracy. [ABSTRACT FROM AUTHOR]

Hydrogel 3D printing holds immense potential in fields like personalized medicine, regenerative therapies, and organ creation, offering biocompatible structures similar to the extracellular matrix. Gelatin‐Methacryloyl (GelMA) emerges as a promising candidate, while its high viscosity poses a significant challenge, especially in vat photopolymerization‐based 3D printing. Here, a new approach is presented by using Hofmeister ionic effect to substantially reduce the viscosity of high‐content (up to 60%) Gelatin bioink at room temperature with enhanced mechanical performance of the printed structures. The thinning effect induced by chaotropic Hofmeister ions is investigated through complex viscosity analysis, optical rotation measurements, and sol–gel conversion studies. The thinning effect induced by chaotropic ions enables precise 3D printing of Gelatin hydrogel, achieving accuracy comparable to prints made with polymers. Furthermore, after polymerization, the cations of the chaotropic salt change their role to cross‐linkers, leading to stronger scaffolds that exhibit biocompatibility with robust cell attachment, proliferation, and suitability for cell growth. The combination facilitates the creation of customizable structures and high printing accuracy will promote the wide application of Gelatin in the development of patient‐specific implants, drug delivery systems, and tissue scaffolds, further improving medical treatment efficacy and personalized healthcare. [ABSTRACT FROM AUTHOR]

This study aimed to reveal the mechanism by which long noncoding RNAs (lncRNAs) modulate hepatocellular carcinoma (HCC) by regulating mRNA via the N6-methyladenosine (m6A) enzyme. The expression and clinical data of 365 HCC patients and 50 healthy control samples were downloaded from the the Cancer Genome Atlas (TCGA) database. Differentially expressed lncRNAs (DElncRNAs) and differentially expressed mRNAs (DEmRNAs) screened using limma packages from the R. m6A2Target database were used to predict the relationship between m6A enzyme-lncRNA and m6A enzyme-mRNA. The mRNAs in the lncRNA-m6A enzyme-mRNA network were subjected to enrichment analysis. Cox regression analysis was used to screen for RNAs significantly related to HCC prognosis. The expression of differentially expressed RNAs (DERs) was verified using the TCGA dataset and GSE55092. Eighty-five DElncRNAs and 747 DEmRNAs were identified. The mRNAs in the lncRNA-m6A enzyme-mRNA network were primarily involved in mitotic cell division, the p53 signaling pathway, and the cell cycle. Three lncRNAs and 14 mRNAs were significantly associated with HCC prognosis. Furthermore, the expression of 12 DERs differed significantly between patients in the early and advanced stages. LINC00665 was predicted to regulate 11 mRNAs by modulating IGF2BP1, IGF2BP2, and YTHDF1. Thus, this study provides new insights into the roles of lncRNA and m6A enzymes in HCC.

Hydrogels have revolutionized the field of soft robotics with their ability to provide dynamic and programmable responses to different stimuli, enabling the fabrication of highly adaptable and flexible robots. This continual development holds significant promise for applications in biomedical devices, active implants, and sensors due to the biocompatibility of hydrogels. Actuation in hydrogel-based soft robotics relies on variations in material properties, structural design, or a combination of both to generate desired movements and behaviors. While such traditional approaches enable hydrogel actuation, they often rely on complex material design, bringing challenges to hydrogel fabrication and hindering practical use. Therefore, this work seeks to present a simplified and versatile approach for fabricating programmable single-component hydrogel-based soft robotics using an encoded building block design concept and 3D printing. A series of structural building blocks have been designed to achieve various actuation characteristics, including the direction, degree, and kinetics of actuation. By assembling these building blocks into various configurations, a broader range of actuation responses can be encoded, allowing for the fabrication of versatile, programmable soft robotics using a single uniform material through vat photopolymerization 3D printing. This approach enables adaptation to a wide range of applications, providing highly customizable encoding designs. [ABSTRACT FROM AUTHOR]

Graphene oxide (GO) has shown significant potential in humidity sensing. It is well accepted that the oxygen-containing functional groups in GO significantly influence its humidity sensing performance. However, the relationship between the content of these groups and the humidity sensing capability of GO-based sensors remains unclear. In the present work, we investigate the role of oxygen-containing functional groups in the humidity sensing performance by oxidizing graphite with mesh numbers 80–120, 325, and 8000 using the Hummers method, resulting in GO-80, GO-325, and GO-8000. Infrared spectroscopy (IR) and X-ray photoelectron spectroscopy (XPS) were used to identify the types and quantification of oxygen-containing functional groups. Molecular dynamics simulation is used to simulate the adsorption energy, intercalation dynamics, and hydrogen bonding of water molecules. Electrochemical tests were used to compare the adsorption/desorption time and response sensitivity of graphene oxide to humidity. It is proposed that hydroxyl and carboxyl groups are the main contributing groups to humidity sensing. GO-8000 shows a relatively fast response time, but the large number of carboxyl groups will hinder intercalation of water molecules, thus exhibiting lower sensitivity. This research provides a reference for the future development of graphene-based sensors, catalysts, and environmental materials. [ABSTRACT FROM AUTHOR]

Vat photopolymerization (VPP) based three‐dimensional (3D) printing, including stereolithography (SLA) and digital light projection (DLP), is known for producing intricate, high‐precision prototypes with superior mechanical properties. However, the challenge lies in the non‐recyclability of covalently crosslinked thermosets used in these printing processes, limiting the sustainable utilization of printed prototypes. This review paper examines the recently explored avenue of VPP 3D‐printed dynamic covalent network (DCN) polymers, which enable reversible crosslinks and allow for the reprocessing of printed prototypes, promoting sustainability. These reversible crosslinks facilitate the rearrangement of crosslinked polymers, providing printed polymers with chemical/physical recyclability, self‐healing capabilities, and degradability. While various mechanisms for DCN polymer systems are explored, this paper focuses solely on photocurable polymers to highlight their potential to revolutionize the sustainability of VPP 3D printing. [ABSTRACT FROM AUTHOR]

A quinoline-based covalent organic framework (N^N)-Q-COF was constructed with chemically stable networks and (N^N)-sites. Its coordination bond with Ir(C^N)2(N^N) units generated new entities with exciting photophysical properties; particularly, the incorporation of a coumarin ligand greatly elevated its properties to a higher level. The further anchoring of NiCl2 produced structures merging both photoredox and organometallic catalysts, resulting in excellent photocatalytic activities in C–S and C–N cross-coupling synthesis. The organic integration of Ir(C^N)2(N^N), NiCl2, and COFs and the advantages of COF networks were responsible for this transcendent dual catalysis. The chemical stability of Q-COFs and the coordination anchoring of Ir(C^N)2(N^N) and NiCl2 enabled its recycling and reuse with no reduction in catalytic performance. [ABSTRACT FROM AUTHOR]

SnO2‐based anode materials are deemed to be one of the most prospective anode materials for Li ion batteries (LIBs) with higher theoretical capacity as compared to conventional graphite. However, the weak electrical conduction and large volume expansion limit its application in eVeryday life. Here, a simple and low‐cost oriented deposition methodology is developed to prepare SnO2@graphite composite, which is obtained based on the redox reaction mechanism between SnSO4 and graphite. SnO2 nanoparticles are uniformly anchored on the carbon layer of graphite. The close anchoring of SnO2 in graphite effectively inhibits the volume expansion of SnO2 during lithiation/delithiation processes, and good combination state between them guarantees the excellent electrical conductivity of the composite. As anode for LIBs, SnO2@graphite electrode delivers superior reversible capacity and provides an improved rate capability compared to graphite electrode. Benefiting from the tight combination of SnO2 and graphite, the Li ion diffusion coefficient of SnO2@graphite electrode is almost twice that of graphite electrode. Pairing with LiFePO4 cathode, the SnO2@graphite/LiFePO4 full cell exhibits higher energy/power densities by comparison with graphite/LiFePO4. Besides, this advanced SnO2@graphite composite material with superior anode behavior can be mass produced by the reported methodology. [ABSTRACT FROM AUTHOR]

Controlling how a material interacts with light is key to optimizing its optical properties to fit a desired function or application. The most straightforward approach is to chemically or physically modify the surface exposed to incident light. An effective method of surface modification is based on the addition of core‐shell structures at the surface. Of particular importance to many technological applications are core‐shell structures with dimensions comparable to the wavelengths of light extending from the UV up to the near‐IR region of the electromagnetic spectrum, which coincides with the major part of the solar spectrum. Surface modification approaches to tailor the optical properties of materials in this range of wavelengths are increasingly relevant in the context of materials and devices used in sustainable energy applications, such as solar cells and heat‐reflective paints. These materials are useful to mitigate the current energy and climate crises by allowing for enhanced energy harvesting and improved thermal management, respectively. Here, recent progress in the fabrication and application of core‐shell micro‐/nano‐structures for the modification of light interaction with surfaces is highlighted. Some limitations and future directions for the design of core‐shell materials are also discussed. [ABSTRACT FROM AUTHOR]

The flexibility and controllability of 3D printing technologies have made them popular for creating integrated wearable sensors. However, producing sensors with 3D anisotropic response is still difficult with current printing materials and techniques. To address this challenge, digital light process (DLP) printing technology with blading function to fabricate 3D anisotropic pressure and strain sensors is used. By applying blading‐induced shearing force, nanoalignment of carbon nanotubes (CNTs) in each layer is achieved, while the continuous liquid interface production of the DLP printing process facilitates good electrical communication between adjacent microlayers. The constructed 3D sensor displays high tensile and pressure responses up to 40 MPa and the ability to detect pressure changes in all directions. Additionally, the resistance anisotropy ratio range is adjustable. Finite‐element method (FEM) simulation shows that the relative change in resistance has an anisotropic distribution that aligns well with experimental results. The work would offer a new way to create 3D anisotropic pressure/strain sensors for advanced flexible and stretchable sensing applications. [ABSTRACT FROM AUTHOR]

Low fracture toughness is the Achilles heel of many polymers for practical application, particularly for long-term and special applications such as medical implants and critical part design. The toughening of polymers is a complicated process, and the toughening mechanisms in many composite systems have not yet been fully understood. Machine learning (ML) has emerged as a promising tool for modeling various complex systems in the field of materials science. In this respect, ML appears to be an effective, affordable, and accurate technique in the modeling of the toughening process. This review article provides an overview of the recent ML-based approach for analyzing toughening effects in rigid particulate-filled polymer composites. The results summarized here would contribute to further possibilities for researchers to explore emerging trends for designing toughing and high-performance polymers. [ABSTRACT FROM AUTHOR]