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Soft materials are usually defined as materials made of mesoscopic entities, often self-organised, sensitive to thermal fluctuations and to weak perturbations. Archetypal examples are colloids, polymers, amphiphiles, liquid crystals, foams. The importance of soft materials in everyday commodity products, as well as in technological applications, is enormous, and controlling or improving their properties is the focus of many efforts. From a fundamental perspective, the possibility of manipulating soft material properties, by tuning interactions between constituents and by applying external perturbations, gives rise to an almost unlimited variety in physical properties. Together with the relative ease to observe and characterise them, this renders soft matter systems powerful model systems to investigate statistical physics phenomena, many of them relevant as well to hard condensed matter systems. Understanding the emerging properties from mesoscale constituents still poses enormous challenges, which have stimulated a wealth of new experimental approaches, including the synthesis of new systems with, e.g. tailored self-assembling properties, or novel experimental techniques in imaging, scattering or rheology. Theoretical and numerical methods, and coarse-grained models, have become central to predict physical properties of soft materials, while computational approaches that also use machine learning tools are playing a progressively major role in many investigations. This Roadmap intends to give a broad overview of recent and possible future activities in the field of soft materials, with experts covering various developments and challenges in material synthesis and characterisation, instrumental, simulation and theoretical methods as well as general concepts.

Soft materials are usually defined as materials made of mesoscopic entities, often self-organised, sensitive to thermal fluctuations and to weak perturbations. Archetypal examples are colloids, polymers, amphiphiles, liquid crystals, foams. The importance of soft materials in everyday commodity products, as well as in technological applications, is enormous, and controlling or improving their properties is the focus of many efforts. From a fundamental perspective, the possibility of manipulating soft material properties, by tuning interactions between constituents and by applying external perturbations, gives rise to an almost unlimited variety in physical properties. Together with the relative ease to observe and characterise them, this renders soft matter systems powerful model systems to investigate statistical physics phenomena, many of them relevant as well to hard condensed matter systems. Understanding the emerging properties from mesoscale constituents still poses enormous challenges, which have stimulated a wealth of new experimental approaches, including the synthesis of new systems with, e.g. tailored self-assembling properties, or novel experimental techniques in imaging, scattering or rheology. Theoretical and numerical methods, and coarse-grained models, have become central to predict physical properties of soft materials, while computational approaches that also use machine learning tools are playing a progressively major role in many investigations. This Roadmap intends to give a broad overview of recent and possible future activities in the field of soft materials, with experts covering various developments and challenges in material synthesis and characterisation, instrumental, simulation and theoretical methods as well as general concepts.

Despite the increased interest in secretomes associated with paracrine/autocrine mechanisms, the majority of mass spectrometric cell secretome studies have been performed using serum-free medium (SFM). On the other hand, serum-containing medium (SCM) is not recommended very much because the secretome obtained with SCM is easily contaminated with fetal bovine serum (FBS) proteins. In this study, through the combination of bioorthogonal non-canonical amino acid tagging (BONCAT) and pulsed-SILAC (pSILAC), we analyzed differentially secreted proteins between SFM and SCM in a cancer-derived human cell, U87MG, and a mesenchymal stem cell derived from human Wharton’s jelly (hWJ-MSCs). In most cases, the bioinformatic tools predicted a protein to be truly secretory when the secretion level of the protein was more in SCM than in SFM. In the case of hWJ-MSCs, the amount of proteins secreted in SCM for 24 hours was larger than that of SFM (log 2 fold change = 0.96), even considering different cell proliferation rates. hWJ-MSCs proteins secreted more in SCM included several positive markers of MSC paracrine factors implicated in angiogenesis, neurogenesis and osteogenesis, and upstream regulators of cell proliferation. Our study suggests the analysis of the secretome should be processed in SCM that promotes cell proliferation and secretion.

Lowering the overpotential of oxygen evolution reaction with electrocatalysts is essential for efficient renewable-electricity-driven electrolysis. Active noble-metal catalysts suffer from leaching and scarcity, while non-noble alternatives face limited intrinsic activity. Here we combine computational guidance with experimental validation to identify atomically dispersed tungsten within NiFe oxyhydroxide, namely W 1 -NiFeOOH, as a promising noble-metal-free oxygen evolution reaction catalyst. An equivariant transformer-based machine-learning interatomic potential accelerates out-of-domain adsorption energy predictions and nominates W 1 -NiFeOOH from 3,976 single-atom-incorporated metal oxyhydroxide configurations. Cyclic-electrodeposited W 1 -NiFeOOH achieves a high current density of 13.1 A cm -2 at 2.0 V and remains stable for 500 hours in alkaline exchange-membrane water electrolysis with commercial membranes. In situ spectroscopy and density functional theory calculations suggest that subsurface W promoter induces synergistic electron redistribution at neighboring Ni-O-Fe edge active sites, thereby lowering the proton-coupled electron-transfer barrier for the deprotonation step and facilitating transformation into the active γ-phase. This integrated computational-experimental workflow provides a blueprint for cost-effective catalyst design for sustainable energy systems. Sustainable hydrogen production requires efficient oxygen evolution catalysts. Here, the authors identify non-noble single-atom catalysts via machine learning-assisted computational screening and reveal their active-site mechanisms for high-performance anion exchange membrane water electrolysis.

Entanglement is a fundamental aspect of quantum physics, both conceptually and for its many applications. Classifying an arbitrary multipartite state as entangled or separable—a task referred to as the separability problem—poses a significant challenge, since a state can be entangled with respect to many different of its partitions. We develop a certification pipeline that feeds the statistics of random local measurements into a non-linear dimensionality reduction algorithm, to determine with respect to which partitions a given quantum state is entangled. After training a model on randomly generated quantum states, entangled in different partitions and of varying purity, we verify the accuracy of its predictions on simulated test data, and finally apply it to states prepared on IBM quantum computing hardware.

The Hawthorne effect is a change in behavior resulting from awareness of being observed or evaluated. This study aimed to determine whether awareness of being evaluated or presence of an observer influence gait. Twenty-one young women were asked to walk in three conditions. In the first condition (unawareness of evaluation; UE), participants were aware that it was a practice trial, and there was no observer. In the second condition (awareness of evaluation; AE), participants were aware that their gait was being evaluated. The third condition (AE + researcher observation; RO) was similar to the second condition except that an additional researcher observed the participant' gait. The spatiotemporal, kinematic, ground reaction forces, and ratio index (symmetry of both lower limbs) were compared among the three conditions. A higher ratio index indicated a relative increase in the value on left versus right. Gait speed (P = 0.012) and stride length (right and left; P = 0.006 and 0.007, respectively) were significantly increased in the AE + RO than in UE. Range of motion of the right hip and left ankle was significantly greater in AE than in UE (P = 0.039 and 0.012, respectively). The ratio index of ground reaction force during push-off was significantly higher in AE and AE + RO conditions than in UE (P < 0.001 and P = 0.004, respectively). The Hawthorne effect (awareness of being evaluated or presence of an observer) potentially influences gait. Thus, factors that influence gait analysis should be considered when evaluating normal gait.

Ruthenium (Ru)‐based electrocatalysts have shown promise for anion exchange membrane water electrolysis (AEMWE) due to their ability to facilitate water dissociation in the hydrogen evolution reaction (HER). However, their performance is limited by strong hydrogen binding, which hinders hydrogen desorption and water re‐adsorption. This study reports the development of RuNi nanoalloys supported on MoO2, which optimize the hydrogen binding strength at Ru sites through modulation by adjacent Ni atoms. Theoretical simulations reveal that substituting Ni atoms for adjacent Ru atoms reduces the high hydrogen adsorption Gibbs free energy on Ru while maintaining a low energy barrier for water dissociation. As a result, the RuNi/MoO₂ catalyst shows excellent HER performance with a low overpotential of 51 mV at a current density of 100 mA cm⁻2, outperforming commercial Pt/C. Furthermore, RuNi/MoO₂ demonstrates high turnover frequency (7.06 s−1), mass activity (13.4 A mg−1), and price activity (1030.77 A dollar−1). In an AEMWE cell, RuNi/MoO₂ as the cathode catalyst achieves a current density of 1 A cm−2 at 60 °C with just 1.7 V using 1 m KOH. This work highlights the potential of RuNi/MoO₂ for ultra‐high mass activity in efficient AEMWE applications. The optimization of hydrogen binding strength on Ru sites is achieved by RuNi nanoalloys for alkaline hydrogen evolution reaction. A finely modulated electronic environment of Ru atoms by adjacent Ni atoms results in near‐zero hydrogen adsorption Gibbs free energy. It shows high catalytic activity, achieving a low overpotential of 51 mV to reach a current density of 100 mA cm−2 and high mass activity of 13.4 A mg−1.

Ruthenium (Ru)‐based electrocatalysts have shown promise for anion exchange membrane water electrolysis (AEMWE) due to their ability to facilitate water dissociation in the hydrogen evolution reaction (HER). However, their performance is limited by strong hydrogen binding, which hinders hydrogen desorption and water re‐adsorption. This study reports the development of RuNi nanoalloys supported on MoO 2 , which optimize the hydrogen binding strength at Ru sites through modulation by adjacent Ni atoms. Theoretical simulations reveal that substituting Ni atoms for adjacent Ru atoms reduces the high hydrogen adsorption Gibbs free energy on Ru while maintaining a low energy barrier for water dissociation. As a result, the RuNi/MoO₂ catalyst shows excellent HER performance with a low overpotential of 51 mV at a current density of 100 mA cm⁻ 2 , outperforming commercial Pt/C. Furthermore, RuNi/MoO₂ demonstrates high turnover frequency (7.06 s −1 ), mass activity (13.4 A mg −1 ), and price activity (1030.77 A dollar −1 ). In an AEMWE cell, RuNi/MoO₂ as the cathode catalyst achieves a current density of 1 A cm −2 at 60 °C with just 1.7 V using 1  m KOH. This work highlights the potential of RuNi/MoO₂ for ultra‐high mass activity in efficient AEMWE applications.

We propose a communication-efficient collaborative inference framework in the domain of edge inference, focusing on the efficient use of vision transformer (ViT) models. The partitioning strategy of conventional collaborative inference fails to reduce communication cost because of the inherent architecture of ViTs maintaining consistent layer dimensions across the entire transformer encoder. Therefore, instead of employing the partitioning strategy, our framework utilizes a lightweight ViT model on the edge device, with the server deploying a complicated ViT model. To enhance communication efficiency and achieve the classification accuracy of the server model, we propose two strategies: 1) attention-aware patch selection and 2) entropy-aware image transmission. Attention-aware patch selection leverages the attention scores generated by the edge device's transformer encoder to identify and select the image patches critical for classification. This strategy enables the edge device to transmit only the essential patches to the server, significantly improving communication efficiency. Entropy-aware image transmission uses min-entropy as a metric to accurately determine whether to depend on the lightweight model on the edge device or to request the inference from the server model. In our framework, the lightweight ViT model on the edge device acts as a semantic encoder, efficiently identifying and selecting the crucial image information required for the classification task. Our experiments demonstrate that the proposed collaborative inference framework can reduce communication overhead by 68% with only a minimal loss in accuracy compared to the server model on the ImageNet dataset.