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Timber densification is a process that has been around since the early 1900s and is predominantly used to enhance the structural properties of timber. The process of densification provides the timber with a greater mechanical strength, hardness, abrasion resistance, and dimensional stability in comparison to its virgin counterparts. It alters the cellular structure of the timber through compression, chemical impregnation, or the combination of the two. This in turn closes the voids of the timber or fills the porosity of the cell wall structure, increasing the density of the timber and, therefore, changing its properties. Several processes are reported in literature which produce densified timber, considering the effect of various parameters, such as the compression ratio, and the temperature on the mechanical properties of the densified timber. This paper presents an overview of the current processes of timber densification and its corresponding effects. The material properties of densified timber, applications, and possible future directions are also explored, as the potential of this innovative material is still not fully realised.

Dense and compact cities yield several benefits for both the population and the environment, including the containment of urban sprawl, reduced carbon emissions, and increased housing supply. Densification of the built environment is thus a key contemporary urban planning paradigm worldwide. However, local residents often oppose urban densification, motivating a need to understand their underlying concerns. In order to do so, we examined different factors driving public acceptance of housing densification projects through a combination of a conjoint survey experiment and different proximity frames among 12,402 participants across Berlin, Chicago, London, Los Angeles, New York, and Paris. Respondents compared housing densification projects with varying attributes, including their geographic proximity, project-related factors, and accompanying planning instruments. The results indicate that the acceptance of such projects decreases with project proximity and that project-related factors, such as the type of investor, usage, and climate goals, impact densification project acceptance. More specifically, we see a negative effect on acceptance levels for projects with for-profit investors and a positive effect when the suggested developments are mixed use or climate neutral. In addition, planning instruments, such as rent control, inclusionary zoning, and participatory planning, appear to positively influence acceptance. Interestingly, a cross-continental comparison shows overall higher acceptance levels of densification by US respondents. These multifaceted results allow us to better understand what drives people’s acceptance of housing projects and how projects and planning processes can be designed to increase democratic acceptance of urban densification.

For the practical use of synthetic hydrogels as artificial biological tissues, flexible electronics, and conductive membranes, achieving requirements for specific mechanical properties is one of the most prominent issues. Here, we demonstrate superstrong, superstiff, and conductive alginate hydrogels with densely interconnecting networks implemented via simple reconstructing processes, consisting of anisotropic densification of pre-gel and a subsequent ionic crosslinking with rehydration. The reconstructed hydrogel exhibits broad ranges of exceptional tensile strengths (8–57 MPa) and elastic moduli (94–1,290 MPa) depending on crosslinking ions. This hydrogel can hold sufficient cations (e.g., Li + ) within its gel matrix without compromising the mechanical performance and exhibits high ionic conductivity enough to be utilized as a gel electrolyte membrane. Further, this strategy can be applied to prepare mechanically outstanding, ionic-/electrical-conductive hydrogels by incorporating conducting polymer within the hydrogel matrix. Such hydrogels are easily laminated with strong interfacial adhesion by superficial de- and re-crosslinking processes, and the resulting layered hydrogel can act as a stable gel electrolyte membrane for an aqueous supercapacitor. Specific mechanical properties are one of the most important issues for application of synthetic hydrogels as biological tissue, flexible electronics or in conductive membranes. Here, the authors demonstrate that a reconstruction process consisting of anisotropic densification of pre-gel and subsequent ionic crosslinking and rehydration leads to strong, stiff, and conductive alginate hydrogels with densely interconnecting networks.

Reducing human reliance on energy-inefficient cooling methods such as air conditioning would have a large impact on the global energy landscape. By a process of complete delignification and densification of wood, we developed a structural material with a mechanical strength of 404.3 megapascals, more than eight times that of natural wood. The cellulose nanofibers in our engineered material backscatter solar radiation and emit strongly in mid-infrared wavelengths, resulting in continuous subambient cooling during both day and night. We model the potential impact of our cooling wood and find energy savings between 20 and 60%, which is most pronounced in hot and dry climates.

Hot isostatic pressing (HIP) is a process that allows producing full dense materials with high mechanical properties. The simulation work undertaken on this subject does not take into account the densification which operating during the rise in pressure and temperature. In this paper, we propose a new method for HIP simulations by considering the powder densification, which operating during the rise in pressure and in temperature. Experiments were performed using tungsten powders by varying the parameters of the used HIP cycles. The obtained results are in good agreement with the simulations predictions, especially when pressures exceed 200 MPa. They clearly show that an important part of the powders densification can reach in some cases 88% during the cycles rise. This approach is very interesting for the number of tuning tests reduction and for the implementation prices improvement.

Separating point clouds into ground and non-ground points is a preliminary and essential step in various applications of airborne light detection and ranging (LiDAR) data, and many filtering algorithms have been proposed to automatically filter ground points. Among them, the progressive triangulated irregular network (TIN) densification filtering (PTDF) algorithm is widely employed due to its robustness and effectiveness. However, the performance of this algorithm usually depends on the detailed initial terrain and the cautious tuning of parameters to cope with various terrains. Consequently, many approaches have been proposed to provide as much detailed initial terrain as possible. However, most of them require many user-defined parameters. Moreover, these parameters are difficult to determine for users. Recently, the cloth simulation filtering (CSF) algorithm has gradually drawn attention because its parameters are few and easy-to-set. CSF can obtain a fine initial terrain, which simultaneously provides a good foundation for parameter threshold estimation of progressive TIN densification (PTD). However, it easily causes misclassification when further refining the initial terrain. To achieve the complementary advantages of CSF and PTDF, a novel filtering algorithm that combines cloth simulation (CS) and PTD is proposed in this study. In the proposed algorithm, a high-quality initial provisional digital terrain model (DTM) is obtained by CS, and the parameter thresholds of PTD are estimated from the initial provisional DTM based on statistical analysis theory. Finally, PTD with adaptive parameter thresholds is used to refine the initial provisional DTM. These contributions of the implementation details achieve accuracy enhancement and resilience to parameter tuning. The experimental results indicate that the proposed algorithm improves performance over their direct predecessors. Furthermore, compared with the publicized improved PTDF algorithms, our algorithm is not only superior in accuracy but also practicality. The fact that the proposed algorithm is of high accuracy and easy-to-use is desirable for users.

Natural structural materials often possess unique combinations of strength and toughness resulting from their complex hierarchical assembly across multiple length scales. However, engineering such well-ordered structures in synthetic materials via a universal and scalable manner still poses a grand challenge. Herein, a simple yet versatile approach is proposed to design hierarchically structured hydrogels by flow-induced alignment of nanofibrils, without high time/energy consumption or cumbersome postprocessing. Highly aligned fibrous configuration and structural densification are successfully achieved in anisotropic hydrogels under ambient conditions, resulting in desired mechanical properties and damage-tolerant architectures, for example, strength of 14 ± 1 MPa, toughness of 154 ± 13 MJ m −3 , and fracture energy of 153 ± 8 kJ m −2 . Moreover, a hydrogel mesoporous framework can deliver ultra-fast and unidirectional water transport (maximum speed at 65.75 mm s −1 ), highlighting its potential for water purification. This scalable fabrication explores a promising strategy for developing bioinspired structural hydrogels, facilitating their practical applications in biomedical and engineering fields. Natural materials can combine strength and toughness, but achieving similar well-ordered structures for synthetic materials is challenging. Here, the authors report hydrogels prepared by flow-induced alignment of nanofibrils, with anisotropic structure and good mechanical properties.

Solid electrolytes are key materials to enable solid-state rechargeable batteries, a promising technology that could address the safety and energy density issues. Here, we report a sulfide sodium-ion conductor, Na 2.88 Sb 0.88 W 0.12 S 4 , with conductivity superior to that of the benchmark electrolyte, Li 10 GeP 2 S 12 . Partial substitution of antimony in Na 3 SbS 4 with tungsten introduces sodium vacancies and tetragonal to cubic phase transition, giving rise to the highest room-temperature conductivity of 32 mS cm −1 for a sintered body, Na 2.88 Sb 0.88 W 0.12 S 4 . Moreover, this sulfide possesses additional advantages including stability against humid atmosphere and densification at much lower sintering temperatures than those (>1000 °C) of typical oxide sodium-ion conductors. The discovery of the fast sodium-ion conductors boosts the ongoing research for solid-state rechargeable battery technology with high safety, cost-effectiveness, large energy and power densities. Solid-state rechargeable batteries using solid electrolytes instead of liquid ones could address the safety and energy density issues. Here the authors report a Na-ion solid electrolyte Na 2.88 Sb 0.88 W 0.12 S 4 which exhibits record high ionic conductivity of 32 mS/cm at room temperature.

Sample-efficient online reinforcement learning often uses replay buffers to store experience for reuse when updating the value function. However, uniform replay is inefficient, since certain classes of transitions can be more relevant to learning. While prioritization of more useful samples is helpful, this strategy can also lead to overfitting, as useful samples are likely to be more rare. In this work, we instead propose a prioritized, parametric version of an agent's memory, using generative models to capture online experience. This paradigm enables (1) densification of past experience, with new generations that benefit from the generative model's generalization capacity and (2) guidance via a family of \"relevance functions\" that push these generations towards more useful parts of an agent's acquired history. We show this recipe can be instantiated using conditional diffusion models and simple relevance functions such as curiosity- or value-based metrics. Our approach consistently improves performance and sample efficiency in both state- and pixel-based domains. We expose the mechanisms underlying these gains, showing how guidance promotes diversity in our generated transitions and reduces overfitting. We also showcase how our approach can train policies with even higher update-to-data ratios than before, opening up avenues to better scale online RL agents.

Wood as an ecofriendly and renewable natural material has been extensively modified through various delignification protocols to preserve its natural structure and fiber direction. The increased porosity and permeability of wood scaffold thus provide excellent opportunities for material infiltration and densification. Wood features in hierarchical structure and biocompatibility are combined with cutting‐edge processings to overcome the weaknesses for vast applications. These new modifications have explored the great potentials of wood as a next‐generation structural and functional material. This review updates the state‐of‐the‐art physicochemical modifications and strategies to prepare versatile wood hydrogels, aerogels, membranes, and fibers with different physicochemical features. Discussion is elaborated to explore the immense breadth of wood as next‐generation material for applications in biomedical, energy storage, sensors, separation, and buildings. Finally, the main challenges of wood scaffold engineering are represented along with potential solutions and directions for developing wood‐based high‐performance structural and functional materials. Wood, as one of the most earth‐abundant biomaterials, has been processed through delignification, densification, and functionalization to deliver large varieties of end‐products for numerous applications. This review updates herein the research advances in transforming wood as next‐generation structural and functional materials for a sustainable future. Guidelines and perspectives for wood engineering are further highlighted.