A Highly Sensitive 3D-Printed Flexible Sensor for Sensing Small Pressures in Deep-Sea High-Pressure Environment.

The origin of life on Earth is believed to be from the ocean, which offers abundant resources in its depths. However, deep-sea operations are limited due to the lack of underwater robots and rigid grippers with sensitive force sensors. Therefore, it is crucial for deep-sea pressure sensors to be integrated with mechanical hands for manipulation. Here, a flexible stress sensor is presented that can function effectively under high water pressure in the deep ocean. Inspired by biological structures found in the abyssal zone, our sensor is designed with an internal and external pressure balance structure (hollow interlocking spherical structure). The digital light processing (DLP) three-dimensional (3D) printing technology is utilized to construct this complex structure after obtaining optimized structural parameters using finite element simulation. The sensor exhibits linear sensitivity of 0.114 kPa-1 within the range of 0-15 kPa and has an extremely short response time of 32 ms, good dynamic-static load response capability, and excellent resistance cycling stability. It shows high sensitivity of 1.74 kPa-1 even under 30 MPa static water pressures and the theoretical working pressure can exceed 110 MPa, which provides a new solution for deep-sea robots.

Microstrain-Stimulated Elastico-Mechanoluminescence with Dual-Mode Stress Sensing.

Elastico-mechanoluminescence technology has shown significant application prospects in stress sensing, artificial skin, remote interaction, and other research areas. Its progress mainly lies in realizing stress visualization and 2D or even 3D stress-sensing effects using a passive sensing mode. However, the widespread promotion of mechanoluminescence (ML) technology is hindered by issues such as high stress or strain thresholds and a single sensing mode based on luminous intensity. In this study, a highly efficient green-emitting ML with dual-mode stress-sensing characteristics driven by microscale strain is developed using LiTaO3:Tb3+. In addition to single-mode sensing based on the luminous intensity, the self-defined parameter (Q) is also introduced as a dual-mode factor for sensing the stress velocity. Impressively, the fabricated LiTaO3:Tb3+ film is capable of generating discernible ML signals even when supplied with strains as low as 500 µst. This is the current minimum strain value that can drive green-emitting ML. This study offers an ideal photonic platform for exploring the potential applications of rare-earth-doped elastico-ML materials in remote interaction devices, high-precision stress sensors, and single-molecule biological imaging.

Sensing at the Nanoscale Using Nitrogen-Vacancy Centers in Diamond: A Model for a Quantum Pressure Sensor.

The sensing of stress under harsh environmental conditions with high resolution has critical importance for a range of applications including earth's subsurface scanning, geological CO2 storage monitoring, and mineral and resource recovery. Using a first-principles density functional theory (DFT) approach combined with the theoretical modelling of the low-energy Hamiltonian, here, we investigate a novel approach to detect unprecedented levels of pressure by taking advantage of the solid-state electronic spin of nitrogen-vacancy (NV) centers in diamond. We computationally explore the effect of strain on the defect band edges and band gaps by varying the lattice parameters of a diamond supercell hosting a single NV center. A low-energy Hamiltonian is developed that includes the effect of stress on the energy level of a ±1 spin manifold at the ground state. By quantifying the energy level shift and split, we predict pressure sensing of up to 0.3 MPa/Hz using the experimentally measured spin dephasing time. We show the superiority of the quantum sensing approach over traditional optical sensing techniques by discussing our results from DFT and theoretical modelling for the frequency shift per unit pressure. Importantly, we propose a quantum manometer that could be useful to measure earth's subsurface vibrations as well as for pressure detection and monitoring in high-temperature superconductivity studies and in material sciences. Our results open avenues for the development of a sensing technology with high sensitivity and resolution under extreme pressure limits that potentially has a wider applicability than the existing pressure sensing technologies.

A Comprehensive Review of In Situ Measurement Techniques for Evaluating the Electro-Chemo-Mechanical Behaviors of Battery Electrodes.

The global production landscape exhibits a substantial need for efficient and clean energy. Enhancing and advancing energy storage systems are a crucial avenue to optimize energy utilization and mitigate costs. Lithium batteries are the most effective and impressive energy utilization system at present, with good safety, high energy density, excellent cycle performance, and other advantages, occupying most of the market. However, due to the defects in the electrode material of the battery itself, the electrode will undergo the process of expansion, stress evolution, and electrode damage during electro-chemical cycling, which will degrade battery performance. Therefore, the detection of property changes in the electrode during electro-chemical cycling, such as the evolution of stress and the modulus change, are useful for preventing the degradation of lithium-ion batteries. This review presents a current overview of measurement systems applied to the performance detection of batteries' electrodes, including the multi-beam optical stress sensor (MOSS) measurement system, the digital image correlation (DIC) measurement system, and the bending curvature measurement system (BCMS), which aims to highlight the measurement principles and advantages of the different systems, summarizes a part of the research methods by using each system, and discusses an effective way to improve the battery performance.

Metallic Wood through Deep-Cell-Wall Metallization: Synthesis and Applications.

Metallic wood combines the unique structural benefits of wood and the properties of metals and is thus promising for applications ranging from heat transfer to electromagnetic shielding to energy conversion. However, achieving metallic wood with full use of wood structural benefits such as anisotropy and multiscale porosity is challenging. A key reason is the limited mass transfer in bulk wood where fibers have closed ends. In this work, programmed removal of cell-wall components (delignification and hemicellulose extraction) was introduced to improve the accessibility of cell walls and mass diffusion in wood. Subsequent low-temperature electroless Cu plating resulted in a uniform continuous Cu coating on the cell wall, and, furthermore, Cu nanoparticles (NPs) insertion into the wood cell wall. A novel Cu NPs-embedded multilayered cell-wall structure was created. The unique structure benefits compressible metal-composite foam, appealing for stress sensors, where the multilayered cell wall contributes to the compressibility and stability. The technology developed for wood metallization here could be transferred to other functionalizations aimed at reaching fine structure in bulk wood.

Lung cancer detection in perioperative patients' exhaled breath with nanomechanical sensor array.

INTRODUCTION: Breath analysis using a chemical sensor array combined with machine learning algorithms may be applicable for detecting and screening lung cancer. In this study, we examined whether perioperative breath analysis can predict the presence of lung cancer using a Membrane-type Surface stress Sensor (MSS) array and machine learning. METHODS: Patients who underwent lung cancer surgery at an academic medical center, Japan, between November 2018 and November 2019 were included. Exhaled breaths were collected just before surgery and about one month after surgery, and analyzed using an MSS array. The array had 12 channels with various receptor materials and provided 12 waveforms from a single exhaled breath sample. Boxplots of the perioperative changes in the expiratory waveforms of each channel were generated and Mann-Whitney U test were performed. An optimal lung cancer prediction model was created and validated using machine learning. RESULTS: Sixty-six patients were enrolled of whom 57 were included in the analysis. Through the comprehensive analysis of the entire dataset, a prototype model for predicting lung cancer was created from the combination of array five channels. The optimal accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 0.809, 0.830, 0.807, 0.806, and 0.812, respectively. CONCLUSION: Breath analysis with MSS and machine learning with careful control of both samples and measurement conditions provided a lung cancer prediction model, demonstrating its capacity for non-invasive screening of lung cancer.

Quantitative characterization of tumor cell traction force on extracellular matrix by hydrogel microsphere stress sensor.

Cell traction force (CTF) is a kind of active force that is a cell senses external environment and actively applies to the contact matrix which is currently a representative stress in cell-extracellular matrix (ECM) interaction. Studying the distribution and variation of CTF during cell-ECM interaction help to explain the impact of physical factors on cell behaviors from the perspective of mechanobiology. However, most of the strategies of characterizing CTF are still limited by the measurement needs in three-dimensional (3D), quantitative characteristics and in vivo condition. Microsphere stress sensor (MSS) as a new type of technology is capable of realizing the quantitative characterization of CTF in 3D and in vivo. Herein, we employed microfluidic platform to design and fabricate MSS which possesses adjustable fluorescent performances, physical properties, and size ranges for better applicable to different cells (3T3, A549). Focusing on the common tumor cells behaviors (adhesion, spreading, and migration) in the process of metastasis, we chose SH-SY5Y as the representative research object in this work. We calculated CTF with the profile and distribution to demonstrate that the normal and shear stress can determined different cell behaviors. Additionally, CTF can also regulate cell adhesion, spreading, and migration in different cell states. Based on this method, the quantitative characterization of CFT of health and disease cells can be achieved, which further help to study and explore the potential mechanism of cell-ECM interaction.

Ceramic Stress Sensor Based on Thick Film Piezo-Resistive Ink for Structural Applications.

This paper presents a ceramic stress sensor with the dimension of a coin, able to measure the compressive force (stress) applied to its two round faces. The sensor is designed and engineered to be embedded inside concrete or masonry structures, like bridges or buildings. It provides good accuracy, robustness, and simplicity of use at potentially low cost for large-scale applications in civil structures. Moreover, it can be calibrated temperature compensated, and it is inherently hermetic, ensuring the protection of sensitive elements from the external environment. It is, therefore, suitable for operating in harsh and dirty environments like civil constructions. The sensor directly measures the internal stress of the structure, exploiting the piezo resistivity of thick film ink based on ruthenium oxide. It is insensitive with respect to the stiffness of the embedding material and the variation of the surrounding material properties like concrete hardening, shrinkage, and creep as it decouples the two components of stress.

Metal-Multilayered Nanomechanical Cantilever Sensor for Detection of Molecular Adsorption.

A metal-multilayered nanomechanical cantilever sensor was proposed to reduce the temperature effect for highly sensitive gas molecular detection. The multilayer structure of the sensor reduces the bimetallic effect, allowing for the detection of differences in molecular adsorption properties on various metal surfaces with higher sensitivity. Our results indicate that the sensor exhibits higher sensitivity to molecules with greater polarity under mixed conditions with nitrogen gas. We demonstrate that stress changes caused by differences in molecular adsorption on different metal surfaces can be detected and that this approach could be used to develop a gas sensor with selectivity for specific gas species.

Repetitive Direct Comparison Method for Odor Sensing.

Olfactory sensors are one of the most anticipated applications of gas sensors. To distinguish odors-complex mixtures of gas species, it is necessary to extract sensor responses originating from the target odors. However, the responses of gas sensors tend to be affected by interfering gases with much higher concentrations than target odor molecules. To realize practical applications of olfactory sensors, extracting minute sensor responses of odors from major interfering gases is required. In this study, we propose a repetitive direct comparison (rDC) method, which can highlight the difference in odors by alternately injecting the two target odors into a gas sensor. We verified the feasibility of the rDC method on chocolates with two different flavors by using a sensor system based on membrane-type surface stress sensors (MSS). The odors of the chocolates were measured by the rDC method, and the signal-to-noise ratios (S/N) of the measurements were evaluated. The results showed that the rDC method achieved improved S/N compared to a typical measurement. The result also indicates that sensing signals could be enhanced for a specific combination of receptor materials of MSS and target odors.

Measurement of Volatile Fatty Acids in Silage through Odors with Nanomechanical Sensors.

The measurement of volatile fatty acids (VFAs) is of great importance in the fields of food and agriculture. There are various methods to measure VFAs, but most methods require specific equipment, making on-site measurements difficult. In this work, we demonstrate the measurements of VFAs in a model sample, silage, through its vapor using an array of nanomechanical sensors-Membrane-type Surface stress Sensors (MSS). Focusing on relatively slow desorption behaviors of VFAs predicted with the sorption kinetics of nanomechanical sensing and the dissociation nature of VFAs, the VFAs can be efficiently measured by using features extracted from the decay curves of the sensing response, resulting in sufficient discrimination of the silage samples. Since the present sensing system does not require expensive, bulky setup and pre-treatment of samples, it has a great potential for practical applications including on-site measurements.

Emerging Wearable Biosensor Technologies for Stress Monitoring and Their Real-World Applications.

Wearable devices are being developed faster and applied more widely. Wearables have been used to monitor movement-related physiological indices, including heartbeat, movement, and other exercise metrics, for health purposes. People are also paying more attention to mental health issues, such as stress management. Wearable devices can be used to monitor emotional status and provide preliminary diagnoses and guided training functions. The nervous system responds to stress, which directly affects eye movements and sweat secretion. Therefore, the changes in brain potential, eye potential, and cortisol content in sweat could be used to interpret emotional changes, fatigue levels, and physiological and psychological stress. To better assess users, stress-sensing devices can be integrated with applications to improve cognitive function, attention, sports performance, learning ability, and stress release. These application-related wearables can be used in medical diagnosis and treatment, such as for attention-deficit hyperactivity disorder (ADHD), traumatic stress syndrome, and insomnia, thus facilitating precision medicine. However, many factors contribute to data errors and incorrect assessments, including the various wearable devices, sensor types, data reception methods, data processing accuracy and algorithms, application reliability and validity, and actual user actions. Therefore, in the future, medical platforms for wearable devices and applications should be developed, and product implementations should be evaluated clinically to confirm product accuracy and perform reliable research.

Detection of Trace Amounts of Water in Organic Solvents by DNA-Based Nanomechanical Sensors.

The detection of trace amounts of water in organic solvents is of great importance in the field of chemistry and in the industry. Karl Fischer titration is known as a classic method and is widely used for detecting trace amounts of water; however, it has some limitations in terms of rapid and direct detection because of its time-consuming sample preparation and specific equipment requirements. Here, we found that a DNA-based nanomechanical sensor exhibits high sensitivity and selectivity to water vapor, leading to the detection and quantification of trace amounts of water in organic solvents as low as 12 ppm in THF, with a ppb level of LoD through their vapors. Since the present method is simple and rapid, it can be an alternative technique to the conventional Karl Fischer titration.

Recent Advances in Nanomechanical Membrane-Type Surface Stress Sensors towards Artificial Olfaction.

Nanomechanical sensors have gained significant attention as powerful tools for detecting, distinguishing, and identifying target analytes, especially odors that are composed of a complex mixture of gaseous molecules. Nanomechanical sensors and their arrays are a promising platform for artificial olfaction in combination with data processing technologies, including machine learning techniques. This paper reviews the background of nanomechanical sensors, especially conventional cantilever-type sensors. Then, we focus on one of the optimized structures for static mode operation, a nanomechanical Membrane-type Surface stress Sensor (MSS), and discuss recent advances in MSS and their applications towards artificial olfaction.

Soft and Stretchable Liquid Metal-Elastomer Composite for Wearable Electronics.

Soft devices, especially capacitive stress (or strain) sensors, are important for applications, including wearable medical monitoring, electronic skin, and soft robotics. The incorporation of liquid metal particles (LMPs) into highly deformable elastomers as inclusions ameliorates the mechanical compliance caused by a rigid filler. The high dielectric constant and liquid feature of LMPs are suitable for soft sensors with high sensitivity and a large real-time dynamic detection range. Here, a class of LM-elastomer composites are introduced with elastic and high dielectric properties, making them uniquely suitable for the application of soft stress sensors. The prepared stretchable soft stress sensor can detect the bending degree of the finger, monitor physiological signals in real time, and distinguish the vibration from the pronunciation of different letters. The nanoscale X-ray computational tomography (nano-CT) measurements indeed detect the changes of LMPs under stress, i.e., LMPs in the matrix distribute from uneven to relatively uniform, agglomerate, and even connect each other to have a conduction path in the composition with high LMP contents, which cause the changes in the physical properties of devices under operation. The cognition of LMP changes in composites under stress is instructive for promoting their further applications in the field of soft devices.

Microdomain Protein Nce102 Is a Local Sensor of Plasma Membrane Sphingolipid Balance.

Sphingolipids are essential building blocks of eukaryotic membranes and important signaling molecules that are regulated tightly in response to environmental and physiological inputs. While their biosynthetic pathway has been well-described, the mechanisms that facilitate the perception of sphingolipid levels at the plasma membrane remain to be uncovered. In Saccharomyces cerevisiae, the Nce102 protein has been proposed to function as a sphingolipid sensor as it changes its plasma membrane distribution in response to sphingolipid biosynthesis inhibition. We show that Nce102 redistributes specifically in regions of increased sphingolipid demand, e.g., membranes of nascent buds. Furthermore, we report that the production of Nce102 increases following sphingolipid biosynthesis inhibition and that Nce102 is internalized when excess sphingolipid precursors are supplied. This finding suggests that the total amount of Nce102 in the plasma membrane is a measure of the current need for sphingolipids, whereas its local distribution marks sites of high sphingolipid demand. The physiological role of Nce102 in the regulation of sphingolipid synthesis is demonstrated by mass spectrometry analysis showing reduced levels of hydroxylated complex sphingolipids in response to heat stress in the nce102Δ deletion mutant. We also demonstrate that Nce102 behaves analogously in the widespread human fungal pathogen Candida albicans, suggesting a conserved principle of local sphingolipid control across species. IMPORTANCE Microorganisms are challenged constantly by their rapidly changing environment. To survive, they have developed diverse mechanisms to quickly perceive stressful situations and adapt to them appropriately. The primary site of both stress sensing and adaptation is the plasma membrane. We identified the yeast protein Nce102 as a marker of local sphingolipid levels and fluidity in the plasma membrane. Nce102 is an important structural and functional component of the membrane compartment Can1 (MCC), a plasma membrane microdomain stabilized by a large cytosolic hemitubular protein scaffold, the eisosome. The MCC/eisosomes are widely conserved among fungi and unicellular algae. To determine if Nce102 carries out similar functions in other organisms, we analyzed the human fungal pathogen Candida albicans and found that Nce102 responds to sphingolipid levels also in this organism, which has potential applications for the development of novel therapeutic approaches. The presented study represents a valuable model for how organisms regulate plasma membrane sphingolipids.

Plant Salinity Sensors: Current Understanding and Future Directions.

Salt stress is a major limiting factor for plant growth and crop yield. High salinity causes osmotic stress followed by ionic stress, both of which disturb plant growth and metabolism. Understanding how plants perceive salt stress will help efforts to improve salt tolerance and ameliorate the effect of salt stress on crop growth. Various sensors and receptors in plants recognize osmotic and ionic stresses and initiate signal transduction and adaptation responses. In the past decade, much progress has been made in identifying the sensors involved in salt stress. Here, we review current knowledge of osmotic sensors and Na+ sensors and their signal transduction pathways, focusing on plant roots under salt stress. Based on bioinformatic analyses, we also discuss possible structures and mechanisms of the candidate sensors. With the rapid decline of arable land, studies on salt-stress sensors and receptors in plants are critical for the future of sustainable agriculture in saline soils. These studies also broadly inform our overall understanding of stress signaling in plants.

Bio-Interface on Freestanding Nanosheet of Microelectromechanical System Optical Interferometric Immunosensor for Label-Free Attomolar Prostate Cancer Marker Detection.

Various biosensors that are based on microfabrication technology have been developed as point-of-care testing devices for disease screening. The Fabry-Pérot interferometric (FPI) surface-stress sensor was developed to improve detection sensitivity by performing label-free biomarker detection as a nanomechanical deflection of a freestanding membrane to adsorb the molecules. However, chemically functionalizing the freestanding nanosheet with excellent stress sensitivity for selective molecular detection may cause the surface chemical reaction to deteriorate the nanosheet quality. In this study, we developed a minimally invasive chemical functionalization technique to create a biosolid interface on the freestanding nanosheet of a microelectromechanical system optical interferometric surface-stress immunosensor. For receptor immobilization, glutaraldehyde cross-linking on the surface of the amino-functionalized parylene membrane reduced the shape variation of the freestanding nanosheet to 1/5-1/10 of the previous study and achieved a yield of 95%. In addition, the FPI surface-stress sensor demonstrated molecular selectivity and concentration dependence for prostate-specific antigen with a dynamic range of concentrations from 100 ag/mL to 1 µg/mL. In addition, the minimum limit of detection of the proposed sensor was 2,000,000 times lower than that of the conventional nanomechanical cantilevers.

The N-terminal cysteine is a dual sensor of oxygen and oxidative stress.

Cellular homeostasis requires the sensing of and adaptation to intracellular oxygen (O2) and reactive oxygen species (ROS). The Arg/N-degron pathway targets proteins that bear destabilizing N-terminal residues for degradation by the proteasome or via autophagy. Under normoxic conditions, the N-terminal Cys (Nt-Cys) residues of specific substrates can be oxidized by dioxygenases such as plant cysteine oxidases and cysteamine (2-aminoethanethiol) dioxygenases and arginylated by ATE1 R-transferases to generate Arg-CysO2(H) (R-CO2). Proteins bearing the R-CO2 N-degron are targeted via Lys48 (K48)-linked ubiquitylation by UBR1/UBR2 N-recognins for proteasomal degradation. During acute hypoxia, such proteins are partially stabilized, owing to decreased Nt-Cys oxidation. Here, we show that if hypoxia is prolonged, the Nt-Cys of regulatory proteins can be chemically oxidized by ROS to generate Arg-CysO3(H) (R-CO3), a lysosomal N-degron. The resulting R-CO3 is bound by KCMF1, a N-recognin that induces K63-linked ubiquitylation, followed by K27-linked ubiquitylation by the noncanonical N-recognin UBR4. Autophagic targeting of Cys/N-degron substrates is mediated by the autophagic N-recognin p62/SQTSM-1/Sequestosome-1 through recognition of K27/K63-linked ubiquitin (Ub) chains. This Cys/N-degron-dependent reprogramming in the proteolytic flux is important for cellular homeostasis under both chronic hypoxia and oxidative stress. A small-compound ligand of p62 is cytoprotective under oxidative stress through its ability to accelerate proteolytic flux of K27/K63-ubiquitylated Cys/N-degron substrates. Our results suggest that the Nt-Cys of conditional Cys/N-degron substrates acts as an acceptor of O2 to maintain both O2 and ROS homeostasis and modulates half-lives of substrates through either the proteasome or lysosome by reprogramming of their Ub codes.

Evaluation of Electromechanical Properties and Conversion Efficiency of Piezoelectric Nanocomposites with Carbon-Fiber-Reinforced Polymer Electrodes for Stress Sensing and Energy Harvesting.

Wireless sensor networks are the future development direction for realizing an Internet of Things society and have been applied in bridges, buildings, spacecraft, and other areas. Nevertheless, with application expansion, the requirements for material performance also increase. Although the development of carbon-fiber-reinforced polymer (CFRP) to achieve these functions is challenging, it has attracted attention because of its excellent performance. This study combined the CFRP electrode with epoxy resin containing potassium sodium niobate piezoelectric nanoparticles and successfully polarized the composite sample. Furthermore, a three-point bending method was applied to compare the bending behavior of the samples. The peak output voltage produced by the maximum bending stress of 98.4 MPa was estimated to be 0.51 mV. Additionally, a conversion efficiency of 0.01546% was obtained. The results showed that the piezoelectric resin with CFRPs as the electrode exhibited stress self-inductance characteristics. This study is expected to be applied in manufacturing self-sensing piezoelectric resin/CFRP composite materials, paving the way for developing stable and efficient self-sensing structures and applications.