A quantitative, multimodal wearable bioelectronic device for comprehensive stress assessment and sub-classification.

Ternary two-dimensional (2D) Nb2SiTe4 has recently garnered significant interest due to its exceptional stability under ambient conditions, a property rarely observed in conventional 2D semiconductors. Despite this advantage, the microscopic mechanism underpinning its environmental robustness remains unresolved. In typical 2D chalcogenides, instability is closely associated with electronically active surface anions, which facilitate the formation of anion vacancies and promote surface oxidation. In this work, we employ first-principles calculations to elucidate the electronic structure and intrinsic defect physics of Nb2SiTe4. Our results demonstrate that Te atoms in Nb2SiTe4 are electronically inert, contributing negligibly to the valence-band maximum as a consequence of weak Te-5p/Nb-4d hybridization. This inert electronic character gives rise to both the high formation energy and the delocalized defect state near the conduction band minimum of the Te vacancy. Removal of a Te atom enables direct Nb–Nb orbital interaction within the face-sharing trigonal prisms, generating an unoccupied bonding state that destabilizes the local coordination environment and renders the formation of Te vacancy energetically unfavorable. Moreover, pristine Nb2SiTe4 exhibits only weak adsorption of the O2 molecule, attributed to the limited electronic perturbation between Te and O2. Even in the presence of a Te vacancy, O2 adsorption induces minimal and highly localized charge transfer, indicating a strongly suppressed oxidation propensity relative to conventional 2D chalcogenides. Our findings reveal the intrinsic origin of the high anion-vacancy formation energy and outstanding oxidation resistance of Nb2SiTe4. This work provides fundamental insight into its exceptional environmental stability and underscores its suitability for reliable and durable 2D electronic applications.

Glassy hydrogels, which maintain a distinctive glassy state at ambient temperature, are appealing for engineering applications. Nevertheless, the existing glassy hydrogels often suffer from inferior strength, inadequate stiffness, and environmental stability caused by weak physical cross-linking. Herein, we present an ultrastiff and ultrastrong glassy hydrogel by introducing robust coordination bonds into a network composed of polyacrylic acid (PAA) and polyvinyl alcohol (PVA), using a copper acetate-assisted strategy. The presence of acetate anions creates a relatively alkaline environment, which deprotonates the carboxyl groups of PAA. This deprotonation exposes carboxylate groups that readily coordinate with copper ions, establishing a densely cross-linked network in the glassy state. The resultant glassy hydrogel exhibits record-breaking Young's modulus (469.7MPa), tensile strength (19.2MPa), and exceptional environmental stability. Moreover, the reversible softening and vitrification induced by the breakage and reforming of coordination bonds endows glassy hydrogel with structural programmability, allowing for the construction of integrated auxetic hydrogel (IAH). The IAH demonstrates enhanced mechanical properties compared to the auxetic skeleton alone while maintaining a negative Poisson's ratio over a wide strain range of 0-29%. This study provides a promising engineering route for the development of advanced glassy hydrogels.

Organic-inorganic formamidinium lead triiodide (FAPbI3) hybrid perovskite quantum dots (QDs) have garnered considerable attention in the photovoltaic field due to their narrow bandgap, exceptional environmental stability, and prolonged carrier lifetime. Unfortunately, their insulating ligands and surface vacancy defects pose significant obstacles to efficient charge transfer across device interfaces. In this work, an electrostatic harmonization strategy at the interface using a donor-acceptor dipole molecular attachment to achieve enhanced charge separation capabilities on the QD surface is ventured. On the basis of theoretical study and experimental evaluation, it has gained a comprehensive understanding of dipole-induced electronic restructuring at the quantum dot interfaces. It reveals that 3-fluoro-4-iodopyridine as a dipole source with appropriate energy levels and a high dipole moment effectively fills surface iodine vacancies (VI-), leading to rapid separation of photoexcited charge carriers. Particularly enhanced hole mobility and hydrophobicity are achieved through strong electronegative effects of fluorine and iodine. Consequently, the FAPbI3 QD solar cells achieve a power conversion efficiency as high as 14.11% with exceptional long-term stability in ambient. These perceptions provide effective interface engineering means for the hybrid perovskite QDs to advance their potential in high-performance optoelectronic applications.

Manipulating perovskite nanocrystals (PNCs) with nanoscale patterning and structural integrity is critical for their integration into advanced optoelectronic devices. However, conventional patterning methods often compromise the structural integrity, detrimental to optical properties of PNCs. Here, a dual‐role strategy is reported that combines PNCs nanopatterning and in situ synthesis directly on versatile substrates, effectively circumventing the post‐synthesis processing damage. A complementary dual‐role mechanism, leveraging molecular coordination and network confinement, enables precise regulation over PNCs growth, yielding tunable crystallite sizes from 20 to 200 nm. Notably, the PNCs exhibit exceptional environmental stability, with photoluminescence intensity enhanced by approximately sixfold after 2000 h of exposure to ambient air. Furthermore, the fabrication of light‐emitting nanocrystal gratings using nanoimprint lithography is demonstrated, achieving sub‐40 nm resolution, precise alignment accuracy, and high aspect ratios, faithfully reproducing the predefined surface morphologies. Tunable optical responses are realized by customizing the PNCs array geometry, thus facilitating amplified directional radiation for sophisticated nanophotonic and optoelectronic applications. These findings establish a robust platform for integrated nano‐optoelectronics, opening avenues for advanced photonic devices and quantum optical applications.

The compound 2,4,6-trichlorophenol poses significant risks to both the aquatic environment and human health. Its inherent persistence and stability present challenges in achieving complete purification, thus warranting its inclusion as a priority pollutant. The present study reports the development of an amphiphilic small-molecule compound that self-assembles into nanovesicles exhibiting remarkable adsorption and photodegradation capabilities. Through the synergistic effects of hydrogen bonding, van der Waals forces, π-π interactions, and electrostatic interactions, these vesicles efficiently adsorb 2,4,6-trichlorophenol from aqueous solutions within 1 min while demonstrating exceptional environmental stability and broad applicability. Upon self-assembly into vesicles, not only are more adsorption sites exposed, but charge separation and migration within the vesicles are also facilitated. Through the synergistic effects of adsorption and photodegradation, complete removal of 2,4,6-trichlorophenol in aqueous solution can be achieved within 8 h while exhibiting excellent recycling capability. This approach offers a viable strategy for designing and synthesizing pure organic photodegradable materials.

2D Ruddlesden-Popper (RP) hybrid perovskite ferroelectrics have emerged as a promising class of direct X-ray detection materials. However, their intrinsic van der Waals gaps result in weak interlayer interactions that destabilize the layered motifs impacting the stability of the X-ray detector. Thus, it is crucial but remains toughly challenge to enhance interlayer interactions exploring stable RP perovskite ferroelectric X-ray detectors. Here, halogen bond is proposed to enhance the interlayer interactions of RP perovskite ferroelectrics obtaining a 2D trilayered ferroelectric, (BrPA)2(EA)2Pb3Br10 (BEPB, BrPA = 3-bromopropylaminium; EA = ethylammonium). Strikingly, the strong Br···Br halogen bonds lock cations to the inorganic skeletons, and C─H···Br hydrogen bonds bridge adjacent spacing sheets, which effectively improves structural stability and suppresses ion migration. The typical P-E hysteresis loops reveal its concrete ferroelectric behaviors, giving a large polarization of ≈7.3 µCcm-2. Consequently, the BEPB-based X-ray detector results in a high sensitivity of 562.6 µC Gy-1 cm-2 at 0 V bias, and most importantly, it exhibits low baseline drift and exceptional environmental stability. As far as is known, halogen bond strengthening 2D multilayered ferroelectric to achieve stable and efficient X-ray detection is unprecedented, which sheds light on the future design of stable optoelectronic devices toward practical applications.

To address aerodynamic efficiency reduction and safety risks caused by wind turbine blade icing in cold regions, this study proposes a low‐energy composite anti‐icing/de‐icing system based on graphite paper and superhydrophobic coatings. The superhydrophobic coating is fabricated from waste silicone rubber derived from discarded insulators. This system synergistically combines the passive anti‐icing capability of the superhydrophobic coating and the active de‐icing performance of electrothermal heating, significantly enhancing the ice prevention and removal efficiency. The experimental results demonstrate that the prepared system surface achieves a contact angle of 157 ± 3° with a rolling angle of 1.6 ± 0.3°, effectively delaying ice formation to 717 s. In the electrothermal mode, the surface temperature reaches 100.8 °C, enabling complete ice removal within 58 s. Robustness tests, including UV aging, icing/de‐icing cycles, and antiwear tests, maintain contact angles above 150°, confirming exceptional environmental stability. The composite system exhibits reliable long‐term performance under extreme conditions while demonstrating advantages in energy efficiency, sustainability, and material recyclability. This study provides an innovative strategy for wind turbine blade anti‐icing/de‐icing and resource utilization of waste polymer materials.

The term "nanozyme" refers to a nanomaterial possessing enzymatic capabilities, and in recent years, the field of nanozymes has experienced rapid advancement. Nanozymes offer distinct advantages over natural enzymes, including ease of production, cost-effectiveness, prolonged storage capabilities, and exceptional environmental stability. In this review, we provide a concise overview of various common applications of nanozymes, encompassing the detection and removal of pollutants such as pathogens, toxic ions, pesticides, phenols, organic contaminants, air pollution, and antibiotic residues. Furthermore, our focus is directed towards the potential challenges and future developments within the realm of nanozymes. The burgeoning applications of nanozymes in bioscience and technology have kindled significant interest in research in this domain, and it is anticipated that nanozymes will soon become a topic of explosive discussion.

2D magnetic oxides have emerged as a pivotal class of materials due to their exceptional environmental stability and tunable magnetic functionalities. Recent advancements in the synthesis of 2D magnetic oxides have opened unprecedented opportunities for next-generation technologies, ranging from spintronics to quantum computing. This review provides a comprehensive analysis of synthesis methodologies and magnetic phenomena in these materials. We first classify 2D magnetic oxides based on structural dimensionality (layered/non-layered) and compositional complexity (binary/ternary systems). Subsequently, we detail advanced vapor-phase synthesis strategies, encompassing additive-assisted growth, spatially confined epitaxy, phase/interface engineering, and heteroatom/heterostructure integration. The intrinsic magnetic properties-including room-temperature ferromagnetism and thickness-dependent spin ordering-are rigorously validated through multimodal characterization techniques such as vibrating sample magnetometry (VSM), magnetic force microscopy (MFM), magneto-optic Kerr effect (MOKE), and superconducting quantum interference device (SQUID) measurements. In addition, we discuss the critical challenges associated with wafer-scale integration and air stability, and propose strategies for designing robust 2D oxide architectures with tailored magnetic responses, providing a roadmap for both fundamental research and device innovation.

Field-effect transistors are crucial components for modern electronics, generating significant research and profitable interest. Metal halide perovskites have recently emerged as a pioneering active material in solar cells, generating interest in their potential use in other electronic and (opto)electronic devices, including field-effect transistors and phototransistors. However, before they can be commercialized, they still face significant challenges owing to their immanent instabilities with respect to heat, moisture, and light. In contrast, due to their exceptional environmental stability, the newly emerging two-dimensional Ruddlesden–Popper type perovskites have garnered significant recognition. The current state of the field is covered in this review article, as are the problems, and a perspective for the scenarios of perovskite field-effect transistors. The effects of temperature, light, and measurement conditions are taken into account, as well as the physics of the device and the fundamental mechanisms that drive these devices, such as ion migration and ionic defects. Subsequently, the performance of perovskite transistors and phototransistors described so far is analyzed and critically evaluated. Finally, the major roadblocks to perovskite transistor advancement are identified and explored. The lessons learned from other perovskite optoelectronic devices are investigated in order to address these obstacles and bring these devices closer to industrial implementation.

Context-aware computing has become an essential component of modern wearable technology, enabling devices to sense, interpret, and respond to users’ environments and affective states. By integrating contextual intelligence, smart systems can adapt their own behavior to better align with user needs, enhancing efficiency, personalization, and user experience. A critical aspect of context awareness is the ability to recognize a user’s internal state, particularly emotions, which influence decision-making, communication, and overall well-being. While emotion recognition is traditionally approached through multimodal sensing—using cameras, microphones, or physiological sensors such as Electroencephalography (EEG), Electrocardiography (ECG), and Electro-dermal Activity (EDA)—these methods often require multiple sensors, leading to challenges related to power consumption, complexity, and privacy. The demand for a more efficient and unobtrusive approach to emotion and context recognition has driven research toward minimal-sensor solutions that can extract meaningful affective and contextual information with reduced hardware requirements. Restricting sensing sources of contextual information by using only limited number of sensors might come at the expense of constraining the ultimate goal of context-awareness. Such expense can be mitigated by “doing more with less” i.e. delving into the opportunities of using a single sensor for multiple purposes. What is desirable is a minimal-sensor solution capable of capturing both motion and physiological signals for both robust emotion recognition and additional context estimation. Among wearable sensors, chest-worn Inertial Measurement Units (IMUs) stand out as promising candidates for comprehensive context estimation, as they are well-positioned to capture motion patterns, and physiological vibrations, being the subtle cardio-respiratory signals. This raises the question of whether a single chest-worn IMU could serve as a minimal yet effective sensor for simultaneously estimating both emotional and contextual information, offering a practical alternative to traditional multi-sensor approaches. To address the challenge of using chest-worn IMU for multiple context sensing, I investigated the potential of using Seismocardiography (SCG) and Accelerometry-Derived Respiration (ADR) to estimate emotional states. First, I explored the existing capabilities of chest-worn IMUs in capturing cardio-respiratory vibrations and motion data, which I used as a knowledge base for embedding such IMUs in context sensing. Data plays a crucial role in this problem, therefore I developed a flexible software interface, ColEmo, to facilitate the scalable collection of emotion-related data and introduced a novel dataset, EmoWear, integrating physiological and motion data for emotion recognition and context awareness. Then, I examined the feasibility of using SCG for emotion recognition, comparing its performance with traditional physiological signals and evaluating its effectiveness as a single-sensor solution. This thesis presents a detailed analysis of the potential of chest-worn IMUs for emotion and context recognition. It reports on the design and validation of a SCG-based wearable system, the development and characteristics of the EmoWear dataset, and the implementation of the ColEmo platform for emotion data collection. The research also examines machine learning techniques applied to the SCG and ADR signals, assessing their effectiveness in estimating emotional states. My findings indicate that SCG-based emotion recognition achieves performance comparable to conventional physiological counterparts, demonstrating the feasibility of using a single IMU sensor for affective computing. The publicly-available EmoWear dataset provides a valuable resource for future research, highlighting the potential of combining motion and physiological data for enhanced context awareness. Additionally, the ColEmo software interface, which I published open-source, proves to be an effective tool for multi-modal emotion data collection, facilitating further advancements in wearable emotion recognition. The results pave the way for a minimal-sensor approach for context information estimation that can significantly reduce system complexity while maintaining high performance. These findings confirm that chest-worn IMUs present an opportunity to further improve emotion recognition in addition to the motion-based context information estimation. My research highlights the need for standardized methodologies in affective computing and demonstrates the viability of motion-based physiological sensing for emotion estimation. By using a single chest-worn IMU, this work presents an alternative to multi-sensor systems, reducing power consumption, and privacy concerns while maintaining robust performance. Future research should focus on refining IMU-based emotion recognition using deep learning techniques and larger datasets to enhance classification accuracy. Further validation in real-world settings is necessary to assess the practical applicability of the proposed system. The EmoWear dataset should be explored for additional context-awareness applications, such as activity recognition and stress monitoring, to maximize its utility. The ColEmo platform offers opportunities for expansion beyond emotion recognition, including cognitive load estimation, stress monitoring, and human-computer interaction. This research moves wearable technology forward by using fewer sensors, helping make affective computing more efficient, versatile, and respectful of privacy.

Monitoring people’s stress levels has become an essential part of behavioural studies for physical and mental illnesses conducted within the biopsychosocial framework. There have been several stress assessment studies in laboratory-based controlled settings. However, the results of these studies do not always translate effectively to an everyday context. The current state of wearable sensor technology allows us to develop systems measuring the physiological signals reflecting stress 24/7 while capturing the context. In this paper, we present a stress monitoring system that provides objective daily stress measurements in everyday settings based on three physiological signals: electrocardiogram (ECG), photoplethysmogram (PPG), and galvanic skin response (GSR) using Shimmer3 ECG, Shimmer3 GSR+, and Empatica E4 wearable sensors. We perform controlled stress assessment experiments on 17 participants in which we successfully detect stress with a 94.55% accuracy for 10-fold cross-validation and an 85.71% accuracy for subject-wise cross-validation. In everyday settings, the system assesses stress with an 81.82% accuracy. We also examine whether motion artefacts affect stress assessment and filter the low-confidence readings to minimise false alarms.

Wearable cameras can gather first-person images of the environment, opening new opportunities for the development of systems able to assist the users in their daily life. This paper studies the problem of recognizing personal contexts from images acquired by wearable devices, which finds useful applications in daily routine analysis and stress monitoring. To assess the influence of different device-specific features, such as the Field Of View and the wearing modality, a dataset of five personal contexts is acquired using four different devices. We propose a benchmark classification pipeline which combines a one-class classifier to detect the negative samples (i.e., images not representing any of the personal contexts under analysis) with a classic one-vs-one multi-class classifier to discriminate among the contexts. Several experiments are designed to compare the performances of many state-of-the-art representations for object and scene classification when used with data acquired by different wearable devices.

Cortisol is a steroid hormone that is released from the body in response to stress. Although a moderate level of cortisol secretion can help the body maintain homeostasis, excessive secretion can cause various diseases, such as depression and anxiety. Conventional methods for cortisol measurement undergo procedures that limit continuous monitoring, typically collecting samples of bodily fluids, followed by separate analysis in a laboratory setting that takes several hours. Thus, recent studies demonstrate wearable, miniaturized sensors integrated with electronic modules that enable wireless real-time analysis. Here, the primary focus is on wearable and implantable electronic devices that continuously measure cortisol concentration. Diverse types of cortisol-sensing techniques, such as antibody-, DNA-aptamer-, and molecularly imprinted polymer-based sensors, as well as wearable and implantable devices that aim to continuously monitor cortisol in a minimally invasive fashion are discussed. In addition to the cortisol monitors that directly measure stress levels, other schemes that indirectly measure stress, such as electrophysiological signals and sweat are also summarized. Finally, the challenges and future directions in stress monitoring and management electronics are reviewed.

The use of low-cost, consumer-grade wearable health monitoring devices has become increasingly prevalent in mental health research, including stress studies. While cortisol response magnitude remains the gold standard for stress assessment, an expanding body of research employs low-cost EEG devices as primary tools for recording biomarker data, often combined with wrist and ring-based wearables. However, the technical variability among low-cost EEG devices, particularly in sensor count and placement according to the 10-20 Electrode Placement System, poses challenges for reproducibility in study outcomes. This review aims to provide an overview of the growing application of low-cost EEG devices and machine learning techniques for assessing brain function, with a focus on stress detection. It also highlights the strengths and weaknesses of various machine learning methods commonly used in stress research, and evaluates the reproducibility of reported findings along with sensor count and placement importance. A comprehensive review was conducted of published studies utilizing EEG devices for stress detection and their associated machine learning approaches. Searches were performed across databases including Scopus, Google Scholar, ScienceDirect, Nature, and PubMed, yielding 69 relevant articles for analysis. The selected studies were synthesized into four thematic categories: stress assessment using EEG, low-cost EEG devices, datasets for EEG-based stress measurement, and machine learning techniques for EEG-based stress analysis. For machine learning-focused studies, validation and reproducibility methods were critically assessed. Study quality was evaluated and scored using the IJMEDI checklist. The review identified several studies employing low-cost EEG devices to monitor brain activity during stress and relaxation phases, with many reporting high predictive accuracy using various machine learning validation techniques. However, only 54% of the studies included health screening prior to experimentation, and 58% were categorized as low-powered due to limited sample sizes. Additionally, few studies validated their results using an independent validation set or cortisol response as a correlating biomarker and there was a lack of consensus on data pre-processing and sensor placement as a key contributor to improving model generalization and accuracy. Low-cost consumer-grade wearable devices, including EEG and wrist-based monitors, are increasingly utilized in stress-related research, offering promising avenues for non-invasive biomarker monitoring. However, significant gaps remain in standardizing EEG signal processing and sensor placement, both of which are critical for enhancing model generalization and accuracy. Furthermore, the limited use of independent validation sets and cortisol response as correlating biomarkers highlights the need for more robust validation methodologies. Future research should focus on addressing these limitations and establishing consensus on data pre-processing techniques and sensor configurations to improve the reliability and reproducibility of findings in this growing field.