Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 6 appraisals
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Hierarchical Structural-Interfacial Engineering with Dynamic Soft-Hard Cross-Linking Enables Full-Range, Ultrasensitive MXene Piezoresistive Sensors
Human tactile perception inspires flexible piezoresistive sensors, yet simultaneously achieving high sensitivity, a wide pressure range, and mechanical robustness remains challenging. Here, we report a hierarchical stress-regulation strategy that integrates multiscale surface microstructures with a dynamically cross-linked MXene/carboxymethyl cellulose/borax sensing network. Replicated microtopographies induce progressive and spatially distributed stress localization, while heterogeneous soft-hard cross-linking regulates nanoscale deformation through adaptive hydrogen bonding and rigid borate anchoring. This coupled structural-interfacial regulation generates abundant stress-concentrated sites, stabilizes conductive pathways, and enables continuous resistance modulation across a broad pressure spectrum. Consequently, the sensor exhibits ultrahigh sensitivity (774.48 kPa-1), a wide working range (334.16 kPa), and fast response/recovery times (8.58/17.22 ms). It reliably captures both subtle physiological signals and large mechanical loads and further supports gesture recognition and robotic control when integrated with real-time feedback and machine learning. This work establishes a general framework for designing robust, full-range tactile sensors through hierarchical stress regulation.
30 May 2026
Read appraisal →Journal of neural engineering
Non-invasive somatotopic proprioceptive feedback for closed-loop robotic hand aperture control
Objective.Current robotic prostheses developed for individuals with transradial amputation often lack physiological feedback, particularly proprioceptive information, which limits control precision and increases reliance on vision.Approach.This study investigates the effect of introducing non-invasive somatotopic feedback using transcutaneous electrical nerve stimulation (TENS) to convey hand aperture information, as an artificial form of proprioceptive feedback. Twenty healthy participants with intact limbs were divided into two groups: one receiving TENS-based feedback and one without feedback. Participants performed an aperture control task under visual and non-visual conditions, with some trials including a concurrent Stroop task to assess cognitive load. We hypothesized that providing non-invasive somatotopic proprioceptive feedback via TENS mitigates key control and integration challenges, leading to improved accuracy, faster learning, and reduced reliance on vision, without increasing cognitive demands.Main results.Under visual deprivation, participants receiving TENS feedback achieved significantly smaller aperture control errors than those without feedback, both under alternating visual conditions (p<0.001) and under prolonged visual deprivation conditions (p=0.017). From the very first trial, TENS-based feedback enabled control accuracy comparable to that with visual input. Although perceptual shifts affected control accuracy under dual-task conditions, the +TENS group maintained high cognitive performance and effective control toward perceived functional targets, suggesting that the artificial feedback nonetheless supported hand control in the presence of a secondary task.Significance.These findings highlight the potential of non-invasive somatotopic proprioceptive feedback delivered through TENS to provide physiologically meaningful information and support future strategies for restoring proprioceptive function in prosthetic hand users.
27 May 2026
Read appraisal →Science advances
Bioinspired ionic thermoreceptors with anisotropic architecture for thermotactile perception in robots
Emulating human skin's ability to perceive temperature and identify material through thermotactile perception is critical for human-machine interaction, robotics operation, and prosthetic sensory feedback systems. However, conventional artificial thermal sensors are largely limited to temperature measurement and cannot replicate the thermotactile-mediated material recognition capabilities inherent in biological systems. Here, we present a bioinspired ionic thermoreceptor with anisotropic thermal response characteristics, enabling high-fidelity material recognition and accurate temperature monitoring. The device incorporates spatially specialized sensing elements that encode thermal signals into modality-specific temporal response patterns, allowing the extraction of the thermal contact coefficient for material discrimination and the absolute temperature for precise thermal monitoring. It achieves high-accuracy material recognition (98.9%) and substantial temperature resolution (0.81 millikelvins). Furthermore, robust covalent bonding between functional layers ensures mechanical durability, signal stability, and environmental resilience. This work establishes a physically grounded and energy-autonomous thermotactile sensing platform for safe, intuitive, and intelligent human-machine interaction.
25 May 2026
Read appraisal →Bioinspiration & biomimetics
Designing McKibben muscles: a critical review for practical implementation
McKibben artificial muscles (AMs) are known as a prominent class of pneumatic actuators in soft robotics and biomechanical engineering, due to their unique structure and multifunctionality. This paper presents a comprehensive review of recent advancements in McKibben AMs, focusing on their performance, structural variations, and operational principles. A systematic literature search on Scopus identified 146 relevant articles, which were analysed for both performance metrics and design characteristics. Inspired by natural muscle behaviour, McKibben AMs enable complex motions such as bending, linear extension, and twisting. These actuators can be organised as individual or bundled systems: individual units are typically arranged in linear or circular patterns, while bundled systems occur in serial, parallel, braided, convergent, or pennate configurations. Recent innovations in smart actuation methods, braided sleeves and internal bladders have expanded their capabilities, enabling embedded sensing, environmental adaptability and untethered operation. Additionally, alternative manufacturing methods offer promising solutions for developing McKibben muscles with enhanced functionality and tailored properties.
19 May 2026
Read appraisal →Proceedings of the National Academy of Sciences of the United States of America
Neural reservoir control of a bio-hybrid soft arm
A long-standing engineering problem, the control of soft robots is difficult because of their highly nonlinear, heterogeneous, anisotropic, and distributed nature. Here, bridging engineering and biology, neural reservoirs are employed for the dynamic control of a bio-hybrid model arm made of multiple muscle-tendon groups enveloping an elastic spine. We show how the use of reservoirs facilitates simultaneous control and self-modeling across challenging tasks, outperforming classic neural network approaches. Further, through the use of spiking reservoirs on neuromorphic hardware, energy efficiency gains of up to 75 and 45 times are obtained relative to standard and high-efficiency CPUs, with implications for the on-board control of untethered, small-scale systems.
28 Apr 2026
Read appraisal →ACS applied materials & interfaces
Evolving Beyond Nature: AI-Driven Rapid Advancements of Bio-Inspired Electronic Skin
Electronic skin (e-skin) is a rapidly developing technology at the intersection of humans and machines with tremendous applications in robotics, healthcare, sports performance, and other fields of wearable electronics. With the recent popularization of artificial intelligence (AI), data-driven approaches to materials design and geometric functional optimization have accelerated e-skin development, enabling optimized microstructures, novel functionalities, and improved usability. Recent progress in bioinspired materials design and functionality enhancements for e-skin is paving the way for future-facing machine learning-powered tools to further accelerate their development. In this review, we discuss the traditional properties of e-skins, their sensing mechanisms, and suitable applications. Then, we investigate and categorize bioinspired structures from material, structural, and functional perspectives. Next, we outline the fundamentals of AI for data-driven optimization and how it can be utilized toward bioinspired e-skin design. This blended e-skin design framework culminates in novel applications, from customizable layouts for personalized therapeutics to dynamic environment recognition for enhanced robotics. Finally, we will explore the challenges of e-skin human-machine interfaces, including their hardware limitations, sensitivity, and longevity issues, and offer insights into future-proof research.
10 Apr 2026
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