Kinetic monitoring of molecular interactions during surfactant-driven self-propelled droplet motion by high spatial resolution waveguide sensing.

HYPOTHESIS: Self-driven actions, like motion, are fundamental characteristics of life. Today, intense research focuses on the kinetics of droplet motion. Quantifying macroscopic motion and exploring the underlying mechanisms are crucial in self-structuring and self-healing materials, advancements in soft robotics, innovations in self-cleaning environmental processes, and progress within the pharmaceutical industry. Usually, the driving forces inducing macroscopic motion act at the molecular scale, making their real-time and high-resolution investigation challenging. Label-free surface sensitive measurements with high lateral resolution could in situ measure both molecular-scale interactions and microscopic motion. EXPERIMENTS: We employ surface-sensitive label-free sensors to investigate the kinetic changes in a self-assembled monolayer of the trimethyl(octadecyl)azanium chloride surfactant on a substrate surface during the self-propelled motion of nitrobenzene droplets. The adsorption-desorption of the surfactant at various concentrations, its removal due to the moving organic droplet, and rebuilding mechanisms at droplet-visited areas are all investigated with excellent time, spatial, and surface mass density resolution. FINDINGS: We discovered concentration dependent velocity fluctuations, estimated the adsorbed amount of surfactant molecules, and revealed multilayer coverage at high concentrations. The desorption rate of surfactant (18.4 s-1) during the microscopic motion of oil droplets was determined by in situ differentiating between droplet visited and non-visited areas.

Boron-doped diamond composites for durable oxygen evolution.

The urgent need to develop efficient, durable, and cost-effective oxygen evolution reaction (OER) catalysts for energy conversion and storage has prompted extensive research. Currently available commercial noble metal-based OER catalysts are expensive and exhibit limited long-term stability. In this study, boron-doped diamond composites (BDDCs) consisting of CoFe and CoFe2C nanoparticles supported by boron-doped diamond (BDD) particles have been prepared. The BDDC catalyst, prepared through a straightforward annealing process, exhibits exceptional durability (up to 72 h at 10 mA cm-2), a low overpotential (306 mV at 10 mA cm-2), and modest Tafel slope (58 mV dec-1). The coherent interfaces between CoFe/CoFe2C nanoparticles and the BDD substrate are essential for enhancing the OER performance. The fabrication method and composite structures presented in this study may facilitate the design and production of promising catalysts.

Optimizing cooperative catalysis of multiple defective interfaces in Pt/mullite catalysts for NO oxidation.

Nitric oxide (NO) oxidation is an integral part of the nitrogen chemical cycle, but competitive activation of NO/O2 over single platinum (Pt)-based catalysts result in inadequate low temperature performance. Here, we constructed catalysts with BiMn2O5/CeO2 and Pt/BiMn2O5 defective interfaces (sufficient activation of NO/O2). The constructed catalyst achieved 95 % NO conversion at 260 °C in NO/O2 atmosphere, superior to most known catalysts. Even after aging (800 °C for 16 h), the NO conversion was up to 76 %. Further, the catalyst can be applied to actual diesel exhaust. Detailed oxygen vacancies (Ov) characterization reveals that BiMn2O5/CeO2 defective interface created by Ce3+-Ov + Mn4+-O ↔ Ce4+-O + Mn3+-Ov promote the activation of NO (on Mn3+ sites) and O2 (on Mn3+-Ov sites). Besides, the Ov on Pt/BiMn2O5 defective interface compensate for the loss of Pt sites ensuring hydrothermal stability. And this construction of multiple defective interfaces develops a pathway for boosting catalytic reactions.

The effect of charge screening for cationic surfactants on the rigidity of interfacial nanoparticle assemblies.

HYPOTHESIS: Functionalizing colloidal particles with oppositely charged surfactants is crucial for stabilizing emulsions, foams, all-liquid structures, and bijels. However, surfactants can reduce the attachment energy, the driving force for colloidal self-assembly at interfaces. An open question remains on how the inherent interfacial activity of cationic surfactants influences the interfacial rigidity of particle-laden interfaces. We hypothesize that charge screening among cationic surfactants regulates the rigidity of oil/water interfaces by reducing the attachment energy of nanoparticles. EXPERIMENTS: We investigate the interfacial rigidity of cetyltrimethylammonium bromide (CTAB) functionalized silica nanoparticles (Ludox® TMA) by analyzing the shape deformation of 1,4-butanediol diacrylate (BDA) droplets under varying salt and alcohol concentrations. The nanoparticle packing density is assessed using scanning electron microscopy. Attachment energy is characterized through interfacial tension measurements, three-phase contact angle analysis, and CTAB adsorption studies. We also examine the effects of interfacial rigidities on the structure of bijel films formed via roll-to-roll solvent transfer-induced phase separation (R2R-STrIPS) using confocal laser scanning microscopy. FINDINGS: Increasing salt and alcohol concentrations decrease the interfacial rigidity of CTAB-functionalized nanoparticle films by reducing the interfacial tension. The contact angle has a minor influence on the rigidity. These results indicate that CTAB charge screening weakens the nanoparticle attachment energy to the interface. Controlling the rigidity enables the mass production of bijel sheets with consistent flatness, which is crucial for their potential applications in catalysis, energy storage, tissue engineering, and filtration membranes.

Tailor-made polymer tracers reveal the role of clay minerals on colloidal transport in carbonate media.

HYPOTHESIS: Host rock weathering and incipient pedogenesis result in the exposition of minerals, e.g., clay minerals in sedimentary limestones. Once exposed, these minerals provide the surfaces for fluid-solid interactions that control the fate of dissolved or suspended compounds such as organic matter and colloids. However, the functional and compositional diversity of organic matter and colloids limits the assessment of reactivity and availability of clay mineral interfaces. Such assessment demands a mobile compound with strong affinity to clay surfaces that is alien to the subsurface. EXPERIMENT: We approached this challenge by using poly(ethylene glycol) (PEG) as interfacial tracer in limestone weathering experiments. FINDINGS: PEG adsorption and transport was dependent on the availability of clay mineral surfaces and carbonate dissolution dynamics. In addition, PEG adsorption featured adsorption-desorption hysteresis which retained PEG mass on clay mineral surfaces. This resulted in different PEG transport for experiments conducted consecutively in the same porous medium. As such, PEG transport was reconstructed with a continuum-scale model parametrized by a Langmuir-type isotherm including hysteresis. Thus, we quantified the influence of exposed clay mineral surfaces on the transport of organic colloids in carbonate media. This renders PEG a suitable model colloid tracer for the assessment of clay surface exposition in porous media.

Wimpled thin films via multiple motions of a bubble decorated with surface-active molecules.

HYPOTHESIS: Thin liquid films play a crucial role in various systems and applications. Understanding the mechanisms that regulate their morphology is a scientific challenge with obvious implications for application optimization. Thin liquid films trapped between bubbles and air-liquid interface can show various configurations influenced by their deformation history and system characteristics. EXPERIMENTS: The morphology of thin liquid films formed in the presence of surface-active molecules is here studied with interferometric techniques. Three different systems with varying interfacial properties are investigated to understand their influence on film morphology. Specific deformation histories are applied to the films to generate complex film structures. FINDINGS: We achieve the creation of a rather stable wimple by implementing controlled bubble motions against the air-liquid interface. We provide a criterion for wimple formation based on lubrication theory. The long-term stability of the wimple is also investigated, and more complex multi-wimple structures are experimentally produced building upon the achieved wimple stability.

Influence of Temporal and Frequency Selective Patterns Combined with CSP Layers on Performance in Exoskeleton-Assisted Motor Imagery Tasks.

Common Spatial Pattern (CSP) has been recognized as a standard and powerful method for the identification of Electroencephalography (EEG)-based Motor Imagery (MI) tasks when implementing brain-computer interface (BCI) systems towards the motor rehabilitation of lost movements. The combination of BCI systems with robotic systems, such as upper limb exoskeletons, has proven to be a reliable tool for neuromotor rehabilitation. Therefore, in this study, the effects of temporal and frequency segmentation combined with layer increase for spatial filtering were evaluated, using three variations of the CSP method for the identification of passive movement vs. MI+passive movement. The passive movements were generated using a left upper-limb exoskeleton to assist flexion/extension tasks at two speeds (high-85 rpm and low-30 rpm). Ten healthy subjects were evaluated in two recording sessions using Linear Discriminant Analysis (LDA) as a classifier, and accuracy (ACC) and False Positive Rate (FPR) as metrics. The results allow concluding that the use of temporal, frequency or spatial selective information does not significantly ( p < 0.05) improve task identification performance. Furthermore, dynamic temporal segmentation strategies may perform better than static segmentation tasks. The findings of this study are a starting point for the exploration of complex MI tasks and their application to neurorehabilitation, as well as the study of brain effects during exoskeleton-assisted MI tasks.

Temporal attention fusion network with custom loss function for EEG--fNIRS classification.

Objective.Methods that can detect brain activities accurately are crucial owing to the increasing prevalence of neurological disorders. In this context, a combination of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) offers a powerful approach to understanding normal and pathological brain functions, thereby overcoming the limitations of each modality, such as susceptibility to artefacts of EEG and limited temporal resolution of fNIRS. However, challenges such as class imbalance and inter-class variability within multisubject data hinder their full potential.Approach.To address this issue, we propose a novel temporal attention fusion network (TAFN) with a custom loss function. The TAFN model incorporates attention mechanisms to its long short-term memory and temporal convolutional layers to accurately capture spatial and temporal dependencies in the EEG--fNIRS data. The custom loss function combines class weights and asymmetric loss terms to ensure the precise classification of cognitive and motor intentions, along with addressing class imbalance issues.Main resultsRigorous testing demonstrated the exceptional cross-subject accuracy of the TAFN, exceeding 99% for cognitive tasks and 97% for motor imagery (MI) tasks. Additionally, the ability of the model to detect subtle differences in epilepsy was analyzed using scalp topography in MI tasks.Significance.This study presents a technique that outperforms traditional methods for detecting high-precision brain activity with subtle differences in the associated patterns. This makes it a promising tool for applications such as epilepsy and seizure detection, in which discerning subtle pattern differences is of paramount importance.

WiFi-Powered Sensor Integrated into a Smart Glove with a Fully Fabric Antenna for the Human-Machine Interface.

The integration of flexible sensors into human-machine interfaces (HMIs) is in increasing demand for intuitive and effective manipulation. Traditional glove-based HMIs, constrained by nonconformal rigid structures or the need for bulky batteries, face limitations in continuous operation. Addressing this, we introduce yarn-based bend sensors in our smart glove, which are wirelessly powered and harvest energy from a fully textile 5.8 GHz WiFi-band antenna receiver. These sensors exhibit a gauge factor (GF) of 5.60 for strains ranging from 0 to 10%. They show a consistent performance regardless of the straining frequency when being stretched and released at frequencies between 0.1 and 0.7 Hz. This reliability ensures that the sensor output is solely dependent on the yarn's elongation. Accurately detecting finger-bending movements from 0° to 90° in a virtual environment, the sensors enable enhanced degrees of freedom for human finger interaction. When integrated with advanced machine-learning techniques, the system achieves a classification accuracy of 98.75% for object recognition, demonstrating its potential for precise and accurate HMI. Unlike conventional near-field energy transfer methods that rely on magnetic flux and are limited by power loss over distance, our fully textile design effectively harvests microwave energy, showing no voltage deterioration up to 1 m away. This minimalist microwave-powered smart glove represents a significant advancement, offering a viable and practical solution for developing intuitive and reliable HMIs.

Nanostructured photonics Probes: A transformative approach in neurotherapeutics and brain circuitry.

Neuroprobes that use nanostructured photonic interfaces are capable of multimodal sensing, stimulation, and imaging with unprecedented spatio-temporal resolution. In addition to electrical recording, optogenetic modulation, high-resolution optical imaging, and molecular sensing, these advanced probes combine nanophotonic waveguides, optical transducers, nanostructured electrodes, and biochemical sensors. The potential of this technology lies in unraveling the mysteries of neural coding principles, mapping functional connectivity in complex brain circuits, and developing new therapeutic interventions for neurological disorders. Nevertheless, achieving the full potential of nanostructured photonic neural probes requires overcoming challenges such as ensuring long-term biocompatibility, integrating nanoscale components at high density, and developing robust data-analysis pipelines. In this review, we summarize and discuss the role of photonics in neural probes, trends in electrode diameter for neural interface technologies, nanophotonic technologies using nanostructured materials, advances in nanofabrication photonics interface engineering, and challenges and opportunities. Finally, interdisciplinary efforts are required to unlock the transformative potential of next-generation neuroscience therapies.

From theoretical aspects to practical food Pickering emulsions: Formation, stabilization, and complexities linked to the use of colloidal food particles.

We noticed that in literature, the term Pickering emulsion (PE) is used as soon as ingredients contain particles, and in this review, we ask ourselves if that is done rightfully so. The basic behavior taking place in particle-stabilized emulsions leads to the conclusion that the desorption energy of particles is generally high making particles highly suited to physically stabilize emulsions. Exceptions are particles with extreme contact angles or systems with very low interfacial tension. Particles used in food and biobased applications are soft, can deform when adsorbed, and most probably have molecules extending into both phases thus increasing desorption energy. Besides, surface-active components will be present either in the ingredients or generated by the emulsification process used, which will reduce the energy of desorption, either by reduced interfacial tension, or changes in the contact angle. In this paper, we describe the relative relevance of these aspects, and how to distinguish them in practice. Practical food emulsions may derive part of their stability from the presence of particles, but most likely have mixed interfaces, and are thus not PEs. Especially when small particles are used to stabilize (sub)micrometer droplets, emulsions may become unstable upon receiving a heat treatment. Stability can be enhanced by connecting the particles or creating network that spans the product, albeit this goes beyond classical Pickering stabilization. Through the architecture of PEs, special functionalities can be created, such as reduction of lipid oxidation, and controlled release features.

Trace Amounts of Multifunctional Electrolyte Additives Enhance Cyclic Stability of High-Rate Aqueous Zinc-Ion Batteries.

Aqueous zinc ion batteries (AZIBs) are renowned for their exceptional safety and eco-friendliness. However, they face cycling stability and reversibility challenges, particularly under high-rate conditions due to corrosion and harmful side reactions. This work introduces fumaric acid (FA) as a trace amount, suitable high-rate, multifunctional, low-cost, and environmentally friendly electrolyte additive to address these issues. FA additives serve as prioritized anchors to form water-poor Inner Helmholtz Plane on Zn anodes and adsorb chemically on Zn anode surfaces to establish a unique in situ solid-electrolyte interface. The combined mechanisms effectively inhibit dendrite growth and suppress interfacial side reactions, resulting in excellent stability of Zn anodes. Consequently, with just tiny quantities of FA, Zn anodes achieve a high Coulombic efficiency (CE) of 99.55 % and exhibit a remarkable lifespan over 2580 hours at 5 mA cm-2, 1 mAh cm-2 in Zn//Zn cells. Even under high-rate conditions (10 mA cm-2, 1 mAh cm-2), it can still run almost for 2020 hours. Additionally, the Zn//V2O5 full cell with FA retains a high specific capacity of 106.95 mAh g-1 after 2000 cycles at 5 A g-1. This work provides a novel additive for the design of electrolytes for high-rate AZIBs.

Overcoming the Tradeoff of Visible Transparency and Electrical Conductance via Dual Smoothing of Dielectric/Metal Interfaces in Cu-Thin-Layer-Based Transparent Electrodes.

The rapid advancement of flexible optoelectronic devices, such as light-emitting diodes, solar cells, and electrochromic devices, necessitates the development of high-performance flexible transparent electrodes (TEs). Dielectric/metal/dielectric (DMD)-type TEs are promising alternatives to conventional indium tin oxide (ITO) due to the high electrical conductance, excellent visible transparency, and sufficient mechanical flexibility. However, the tradeoff between electrical conductance and visible transparency poses a challenge to performance enhancement. This study introduces an Ar-ion-mediated interface modification method to address this tradeoff by dual smoothing of dielectric/metal interfaces in TiOx/Cu/ZnO TEs. Implementing this dual smoothing methodology significantly enhances both electrical conductance and visible light transmittance, achieving a Haacke figure of merit 200% higher than that of an unmodified otherwise identical structure. The highest figure of merit is 0.113 Ω-1, a record high for Cu-thin-layer-based DMD TEs, far surpassing ITO electrode values. Further, the enhanced optoelectronic performance remains highly durable under severe and simultaneous electrical, thermal, and mechanical stresses, showcasing the potential for significant advances in flexible optoelectronics.

Neural interfaces: Bridging the brain to the world beyond healthcare.

Neural interfaces, emerging at the intersection of neurotechnology and urban planning, promise to transform how we interact with our surroundings and communicate. By recording and decoding neural signals, these interfaces facilitate direct connections between the brain and external devices, enabling seamless information exchange and shared experiences. Nevertheless, their development is challenged by complexities in materials science, electrochemistry, and algorithmic design. Electrophysiological crosstalk and the mismatch between electrode rigidity and tissue flexibility further complicate signal fidelity and biocompatibility. Recent closed-loop brain-computer interfaces, while promising for mood regulation and cognitive enhancement, are limited by decoding accuracy and the adaptability of user interfaces. This perspective outlines these challenges and discusses the progress in neural interfaces, contrasting non-invasive and invasive approaches, and explores the dynamics between stimulation and direct interfacing. Emphasis is placed on applications beyond healthcare, highlighting the need for implantable interfaces with high-resolution recording and stimulation capabilities.

Multimodal human computer interaction of wheelchairs supporting lower limb active rehabilitation.

Currently, an important challenge in stroke rehabilitation is how to effectively restore motor functions of lower limbs. This paper presents multimodal human computer interaction (HCI) of wheelchairs supporting lower limb active rehabilitation. First, multimodal HCI incorporating motor imagery electroencephalography (EEG), electromyography (EMG) and speech is designed. Second, prototype supporting wheelchair active rehabilitation method is illustrated in details. Third, the preliminary brain-computer interfaces (BCI) and speech recognition task experiments are carried out respectively, and the results are obtained. Finally, discussion is conducted and conclusion is drawn. This study has important practical significance in auxiliary movements and neurorehabilitation for stroke patients.

Crystal Patterning from Aqueous Solutions via Solutal Instabilities.

Fluid instabilities can be harnessed for facile self-assembly of patterned structures on the nano- and microscale. Evaporative self-assembly from drops is one simple technique that enables a range of patterning behaviors due to the multitude of fluid instabilities that arise due to the simultaneous existence of temperature and solutal gradients. However, the method suffers from limited controllability over patterns that can arise and their morphology. Here, we demonstrate that a range of distinct crystalline patterns including hexagonal arrays, branches, and sawtooth structures emerge from evaporation of water drops containing calcium sulfate on hydrophilic and superhydrophilic substrates. Different pattern regimes emerge as a function of contact line dynamics and evaporation rates, which dictate which fluid instabilities are most likely to emerge. The underlying physical mechanisms behind instability for controlled self-assembly involve Marangoni flows and forced wetting/dewetting. We also demonstrate that these patterns composed of water-soluble inorganic crystals can serve as sustainable and easily removable masks for applications in microscale fabrication.

A Novel Anion Receptor Additive for -40ºC Sodium Metal Batteries by Anion/Cation Solvation Engineering.

Sodium metal batteries, known for their high theoretical specific capacity, abundant reserves, and promising low-temperature performance, have garnered significant attention. However, the large ionic radius of Na+ and sluggish transport kinetics across the interfacial structure hinder their practical application. Previous reviews have rarely regulated electrolyte performance from the perspective of anions; as important components of the electrolyte, the regulation mechanism is not well understood. Herein, a novel anion receptor additive, 4-aminophenylboronic acid pinalol ester (ABAPE), is proposed to weaken the coupling between anions and cations and accelerate Na+ transport kinetics. The results of theoretical calculations and X-ray photoelectron spectroscopy with deep Ar-ion etching demonstrate that the introduction of this additive alters the solvation structure of Na+, reduces the desolvation barrier and forms a stable and dense electrode-electrolyte interface. Moreover, ABAPE forms hydrogen bonds (-NH···O/F) with H2O/HF, effectively preventing the hydrolysis of NaPF6 and stabilizing acidic species. Consequently, the Na||Na symmetric cell exhibits excellent long-cycle performance of 500 h at 1 mA cm-2 and 0.5 mAh cm-2. The Na||Na3V2(PO4)3 (NVP) full cell with the addition of ABAPE maintains a capacity retention of 84.29% at 1 C after 1200 cycles and presents no capacity decay over 150 cycles at -40°C.

Geometric neural network based on phase space for BCI-EEG decoding.

\textbf{Objective:} The integration of Deep Learning (DL) algorithms on brain signal analysis is still in its nascent stages compared to their success in fields like Computer Vision. This is particularly true for BCI, where the brain activity is decoded to control external devices without requiring muscle control. Electroencephalography (EEG) is a widely adopted choice for designing BCI systems due to its non-invasive and cost-effective nature and excellent temporal resolution. Still, it comes at the expense of limited training data, poor signal-to-noise, and a large variability across and within-subject recordings. Finally, setting up a BCI system with many electrodes takes a long time, hindering the widespread adoption of reliable DL architectures in BCIs outside research laboratories. To improve adoption, we need to improve user comfort using, for instance, reliable algorithms that operate with few electrodes. \textbf{Approach:} Our research aims to develop a DL algorithm that delivers effective results with a limited number of electrodes. Taking advantage of the Augmented Covariance Method and the framework of SPDNet, we propose the \method{} architecture and analyze its performance and the interpretability of the results. The evaluation is conducted on 5-fold cross-validation, using only three electrodes positioned above the Motor Cortex. The methodology was tested on nearly 100 subjects from several open-source datasets using the Mother Of All BCI Benchmark (MOABB) framework. \textbf{Main results:} The results of our \method{} demonstrate that the augmented approach combined with the SPDNet significantly outperforms all the current state-of-the-art DL architecture in MI decoding. \textbf{Significance:} This new architecture is explainable and with a low number of trainable parameters.

A comprehensive Review into Emission Sources, Formation Mechanisms, Ecological Effects, and Biotransformation Routes of Halogenated Polycyclic Aromatic Hydrocarbons (HPAHs).

Halogenated polycyclic aromatic hydrocarbons (HPAHs, H = F, Cl, Br) are a new class of PAHs derivatives that mainly originate from the incomplete combustion of halogen-laden materials and via metallurgical operations. These compounds circulate extensively in various environmental matrices. This survey provides a comprehensive review on governing synthesis routes of HPAHs, their environmental occurrence, and their health and ecological effects. The review comprehensively enlists and presents emission sources of these emerging organic pollutants into the air that serves as their main reservoir. The formation of HPAHs ensues through successive addition reactions of related precursors accompanied by ring cyclization steps; in addition to direct unimolecular fragmentation of parents halogenated. Halogenation of parent PAHs rapidly occurs in saline ecosystems, thus multiplying the availability of these notorious compounds in the environment. Certain HPAHs appear to be more carcinogenic than dioxins. Transmission routes of HPAHs from their emission sources to water bodies, soil, aquatic life, plants, terrestrial animals, and humans are well-documented. Later, the direct and indirect diffusion of HPAHs from air to the biotic (plants, animals, humans) and abiotic components (soil, water, sediments) are described in detail. The study concludes that HPAHs are permeable to the carbon matrices resulting in the alleviation of the source-to-sink interface. As a potential future perspective, understanding the transmission interfaces lays a foundation to intervene in the introduction of these toxicants into the food chain.

Temperature-Dependent Surface Anisotropy in (110) Epitaxial Rare Earth Iron Garnet Films.

Ferrimagnetic oxide thin films are important material platforms for spintronic devices. Films grown on low symmetry orientations such as (110) exhibit complex anisotropy landscapes that can provide insight into novel phenomena such as spin-torque auto-oscillation and spin superfluidity. Using spin-Hall magnetoresistance measurements, the in-plane (IP) and out-of-plane (OOP) uniaxial anisotropy energies are determined for a thickness series (5-50 nm) of europium iron garnet (EuIG) and thulium iron garnet (TmIG) films epitaxially grown on a gadolinium gallium substrate with (110) orientation and capped with Pt. Pt/EuIG/GGG exhibits an (001) easy plane of magnetization perpendicular to the substrate, whereas Pt/TmIG/GGG exhibits an (001) hard plane of magnetization perpendicular to the substrate with an IP easy axis. Both IP and OOP surface anisotropy energies comparable in magnitude to the bulk anisotropy are observed. The temperature dependence of the surface anisotropies is consistent with first-order predictions of a simplified Néel surface anisotropy model. By taking advantage of the thickness and temperature dependence demonstrated in these ferrimagnetic oxides grown on the low symmetry (110) orientations, the complex anisotropy landscapes can be tuned to act as a platform to explore rich spin textures and dynamics.