Real-Time and Wireless Transmission of a Nitrogen-Doped Ti3C2Tx Wearable Gas Sensor for Efficient Detection of Food Spoilage and Ammonia Leakage.

With the rising popularity of smart homes, there is an urgent need for devices that can perform real-time online detection of ammonia (NH3) concentrations for food quality measurement. In addition, timely warning is crucial to preventing individual deaths from NH3. However, few studies can realize continuous monitoring of NH3 with high stability and subsequent application validation. Herein, we report on an integrated device equipped with a nitrogen-doped Ti3C2Tx gas sensor that shows great potential in detecting food spoilage and NH3 leakage. The nitrogen doping results in the lattice misalignment of Ti3C2Tx, subsequently realizing effective barrier height modulation and enhanced charge transfer efficiency of nitrogen-doped Ti3C2Tx. Density functional theory calculations confirm the greatly enhanced adsorption of NH3 on nitrogen-doped Ti3C2Tx. Our work can inspire the design of efficient gas sensors for real-time and wireless detection of food spoilage and NH3 leakage.

Quantum Confinement and End-Sealing Effects for Highly Sensitive and Stable Nitrogen Dioxide Detection: Homogeneous Integration of Ti3C2Tx-Based Flexible Gas Sensors.

The real-time and room-temperature detection of nitrogen dioxide (NO2) holds significant importance for environmental monitoring. However, the performance of NO2 sensors has been hampered by the trade-off between the high sensitivity and stability of conventional sensitive materials. Here, we present a novel fully flexible paper-based gas sensing structure by combining a homogeneous screen-printed titanium carbide (Ti3C2Tx) MXene-based nonmetallic electrode with a MoS2 quantum dots/Ti3C2Tx (MoS2 QDs/Ti3C2Tx) gas-sensing film. These precisely designed gas sensors demonstrate an improved response value (16.3% at 5 ppm) and a low theoretical detection limit of 12.1 ppb toward NO2, which exhibit a remarkable 3.5-fold increase in sensitivity compared to conventional Au interdigital electrodes. The outstanding performance can be attributed to the integration of the quantum confinement effect of MoS2 QDs and the conductivity of Ti3C2Tx, establishing the main active adsorption sites and enhanced charge transport pathways. Furthermore, an end-sealing effect strategy was applied to decorate the defect sites with naturally oxygen-rich tannic acid and conductive polymer, and the formed hydrogen bonding network at the interface effectively mitigated the oxidative degradation of the Ti3C2Tx-based gas sensors. The exceptional stability has been achieved with only a 1.8% decrease in response over 4 weeks. This work highlights the innovative design of high-performance gas sensing materials and homogeneous gas sensor techniques.

Highly Sensitive Ammonia Gas Sensors at Room Temperature Based on the Catalytic Mechanism of N, C Coordinated Ni Single-Atom Active Center.

Significant challenges are posed by the limitations of gas sensing mechanisms for trace-level detection of ammonia (NH3). In this study, we propose to exploit single-atom catalytic activation and targeted adsorption properties to achieve highly sensitive and selective NH3 gas detection. Specifically, Ni single-atom active sites based on N, C coordination (Ni-N-C) were interfacially confined on the surface of two-dimensional (2D) MXene nanosheets (Ni-N-C/Ti3C2Tx), and a fully flexible gas sensor (MNPE-Ni-N-C/Ti3C2Tx) was integrated. The sensor demonstrates a remarkable response value to 5 ppm NH3 (27.3%), excellent selectivity for NH3, and a low theoretical detection limit of 12.1 ppb. Simulation analysis by density functional calculation reveals that the Ni single-atom center with N, C coordination exhibits specific targeted adsorption properties for NH3. Additionally, its catalytic activation effect effectively reduces the Gibbs free energy of the sensing elemental reaction, while its electronic structure promotes the spill-over effect of reactive oxygen species at the gas-solid interface. The sensor has a dual-channel sensing mechanism of both chemical and electronic sensitization, which facilitates efficient electron transfer to the 2D MXene conductive network, resulting in the formation of the NH3 gas molecule sensing signal. Furthermore, the passivation of MXene edge defects by a conjugated hydrogen bond network enhances the long-term stability of MXene-based electrodes under high humidity conditions. This work achieves highly sensitive room-temperature NH3 gas detection based on the catalytic mechanism of Ni single-atom active center with N, C coordination, which provides a novel gas sensing mechanism for room-temperature trace gas detection research.

Wide-range and high-accuracy wireless sensor with self-humidity compensation for real-time ammonia monitoring.

Real-time and accurate biomarker detection is highly desired in point-of-care diagnosis, food freshness monitoring, and hazardous leakage warning. However, achieving such an objective with existing technologies is still challenging. Herein, we demonstrate a wireless inductor-capacitor (LC) chemical sensor based on platinum-doped partially deprotonated-polypyrrole (Pt-PPy+ and PPy0) for real-time and accurate ammonia (NH3) detection. With the chemically wide-range tunability of PPy in conductivity to modulate the impedance, the LC sensor exhibits an up-to-180% improvement in return loss (S11). The Pt-PPy+ and PPy0 shows the p-type semiconductor nature with greatly-manifested adsorption-charge transfer dynamics toward NH3, leading to an unprecedented NH3 sensing range. The S11 and frequency of the Pt-PPy+ and PPy0-based sensor exhibit discriminative response behaviors to humidity and NH3, enabling the without-external-calibration compensation and accurate NH3 detection. A portable system combining the proposed wireless chemical sensor and a handheld instrument is validated, which aids in rationalizing strategies for individuals toward various scenarios.

Quality Evaluation of Lonicerae Flos Produced in Southwest China Based on HPLC Analysis and Antioxidant Activity.

Lonicera macranthoides, the main source of traditional Chinese medicine Lonicerae Flos, is extensively cultivated in Southwest China. However, the quality of L. macranthoides produced in this region significantly varies due to its wide distribution and various cultivation breeds. Herein, 50 Lonicerae Flos samples derived from different breeds of L. macranthoides cultivated in Southwest China were collected for quality evaluation. Six organic acids and three saponin compounds were quantitatively analyzed using HPLC. Furthermore, the antioxidant activity of a portion of samples was conducted with 2,2'-Azinobis-(3-ethylbenzthiazoline-6-sulphonate) (ABTS) and 1,1-diphenyl-2-picryl-hydrazyl (DPPH) radical scavenging experiments. According to the quantitative results, all samples met the quality standards outlined in the Chinese Pharmacopoeia. The samples from Guizhou, whether derived from unopened or open wild-type breeds, exhibited high quality, while the wild-type samples showed relatively significant fluctuation in quality. The samples from Chongqing and Hunan demonstrated similar quality, whereas those from Sichuan exhibited relatively lower quality. These samples demonstrated significant abilities in clearing ABTS and DPPH radicals. The relationship between HPLC chromatograms and antioxidant activity, as elucidated by multivariate analysis, indicated that chlorogenic acid, isochlorogenic acid A, isochlorogenic acid B, and isochlorogenic acid C are active components and can serve as Q-markers for quality evaluation.

Biomechanical Investigation of Lower Limbs during Slope Transformation Running with Different Longitudinal Bending Stiffness Shoes.

BACKGROUND: During city running or marathon races, shifts in level ground and up-and-down slopes are regularly encountered, resulting in changes in lower limb biomechanics. The longitudinal bending stiffness of the running shoe affects the running performance. PURPOSE: This research aimed to investigate the biomechanical changes in the lower limbs when transitioning from level ground to an uphill slope under different longitudinal bending stiffness (LBS) levels in running shoes. METHODS: Fifteen male amateur runners were recruited and tested while wearing three different LBS running shoes. The participants were asked to pass the force platform with their right foot at a speed of 3.3 m/s ± 0.2. Kinematics data and GRFs were collected synchronously. Each participant completed and recorded ten successful experiments per pair of shoes. RESULTS: The range of motion in the sagittal of the knee joint was reduced with the increase in the longitudinal bending stiffness. Positive work was increased in the sagittal plane of the ankle joint and reduced in the keen joint. The negative work of the knee joint increased in the sagittal plane. The positive work of the metatarsophalangeal joint in the sagittal plane increased. CONCLUSION: Transitioning from running on a level surface to running uphill, while wearing running shoes with high LBS, could lead to improved efficiency in lower limb function. However, the higher LBS of running shoes increases the energy absorption of the knee joint, potentially increasing the risk of knee injuries. Thus, amateurs should choose running shoes with optimal stiffness when running.

Can the Entire Function of the Foot Be Concentrated in the Forefoot Area during the Running Stance Phase? A Finite Element Study of Different Shoe Soles.

The goal of this study was to use the finite element (FE) method to compare and study the differences between bionic shoes (BS) and normal shoes (NS) forefoot strike patterns when running. In addition, we separated the forefoot area when forefoot running as a way to create a small and independent area of instability. An adult male of Chinese descent was recruited for this investigation (age: 26 years old; body height: 185 cm; body mass: 82 kg) (forefoot strike patterns). We analyzed forefoot running under two different conditions through FE analysis, and used bone stress distribution feature classification and recognition for further analysis. The metatarsal stress values in forefoot strike patterns with BS were less than with NS. Additionally, the bone stress classification of features and the recognition accuracy rate of metatarsal (MT) 2, MT3 and MT5 were higher than other foot bones in the first 5%, 10%, 20% and 50% of nodes. BS forefoot running helped reduce the probability of occurrence of metatarsal stress fractures. In addition, the findings further revealed that BS may have important implications for the prevention of hallux valgus, which may be more effective in adolescent children. Finally, this study presents a post-processing method for FE results, which is of great significance for further understanding and exploration of FE results.

New Insights Optimize Landing Strategies to Reduce Lower Limb Injury Risk.

Single-leg landing (SL) is often associated with a high injury risk, especially anterior cruciate ligament (ACL) injuries and lateral ankle sprain. This work investigates the relationship between ankle motion patterns (ankle initial contact angle [AICA] and ankle range of motion [AROM]) and the lower limb injury risk during SL, and proposes an optimized landing strategy that can reduce the injury risk. To more realistically revert and simulate the ACL injury mechanics, we developed a knee musculoskeletal model that reverts the ACL ligament to a nonlinear short-term viscoelastic mechanical mechanism (strain rate-dependent) generated by the dense connective tissue as a function of strain. Sixty healthy male subjects were recruited to collect biomechanics data during SL. The correlation analysis was conducted to explore the relationship between AICA, AROM, and peak vertical ground reaction force (PVGRF), joint total energy dissipation (TED), peak ankle knee hip sagittal moment, peak ankle inversion angle (PAIA), and peak ACL force (PAF). AICA exhibits a negative correlation with PVGRF (r = -0.591) and PAF (r = -0.554), and a positive correlation with TED (r = 0.490) and PAIA (r = 0.502). AROM exhibits a positive correlation with TED (r = 0.687) and PAIA (r = 0.600). The results suggested that the appropriate increases in AICA (30° to 40°) and AROM (50° to 70°) may reduce the lower limb injury risk. This study has the potential to offer novel perspectives on the optimized application of landing strategies, thus giving the crucial theoretical basis for decreasing injury risk.

A new method applied for explaining the landing patterns: Interpretability analysis of machine learning.

As one of many fundamental sports techniques, the landing maneuver is also frequently used in clinical injury screening and diagnosis. However, the landing patterns are different under different constraints, which will cause great difficulties for clinical experts in clinical diagnosis. Machine learning (ML) have been very successful in solving a variety of clinical diagnosis tasks, but they all have the disadvantage of being black boxes and rarely provide and explain useful information about the reasons for making a particular decision. The current work validates the feasibility of applying an explainable ML (XML) model constructed by Layer-wise Relevance Propagation (LRP) for landing pattern recognition in clinical biomechanics. This study collected 560 groups landing data. By incorporating these landing data into the XML model as input signals, the prediction results were interpreted based on the relevance score (RS) derived from LRP. The interpretation obtained from XML was evaluated comprehensively from the statistical perspective based on Statistical Parametric Mapping (SPM) and Effect Size. The RS has excellent statistical characteristics in the interpretation of landing patterns between classes, and also conforms to the clinical characteristics of landing pattern recognition. The current work highlights the applicability of XML methods that can not only satisfy the traditional decision problem between classes, but also largely solve the lack of transparency in landing pattern recognition. We provide a feasible framework for realizing interpretability of ML decision results in landing analysis, providing a methodological reference and solid foundation for future clinical diagnosis and biomechanical analysis.

Mesoporous and Encapsulated In2O3/Ti3C2Tx Schottky Heterojunctions for Rapid and ppb-Level NO2 Detection at Room Temperature.

Rapid and ultrasensitive detection of toxic gases at room temperature is highly desired in health protection but presents grand challenges in the sensing materials reported so far. Here, we present a gas sensor based on novel zero dimensional (0D)/two dimensional (2D) indium oxide (In2O3)/titanium carbide (Ti3C2Tx) Schottky heterostructures with a high surface area and rich oxygen vacancies for parts per billion (ppb) level nitrogen dioxide (NO2) detection at room temperature. The In2O3/Ti3C2Tx gas sensor exhibits a fast response time (4 s), good response (193.45% to 250 ppb NO2), high selectivity, and excellent cycling stability. The rich surface oxygen vacancies play the role of active sites for the adsorption of NO2 molecules, and the Schottky junctions effectively adjust the charge-transfer behavior through the conduction tunnel in the sensing material. Furthermore, In2O3 nanoparticles almost fully cover the Ti3C2Tx nanosheets which can avoid the oxidation of Ti3C2Tx, thus contributing to the good cycling stability of the sensing materials. This work sheds light on the sensing mechanism of heterojunction nanostructures and provides an efficient pathway to construct high-performance gas sensors through the rational design of active sites.

Effects of different contact angles during forefoot running on the stresses of the foot bones: a finite element simulation study.

Introduction: The purpose of this study was to compare the changes in foot at different sole-ground contact angles during forefoot running. This study tried to help forefoot runners better control and improve their technical movements by comparing different sole-ground contact angles. Methods: A male participant of Chinese ethnicity was enlisted for the present study, with a recorded age of 25 years, a height of 183 cm, and a body weight of 80 kg. This study focused on forefoot strike patterns through FE analysis. Results: It can be seen that the peak von Mises stress of M1-5 (Metatarsal) of a (Contact angle: 9.54) is greater than that of b (Contact angle: 7.58) and c (Contact angle: 5.62) in the three cases. On the contrary, the peak von Mises stress of MC (Medial Cuneiform), IC (Intermediate Cuneiform), LC (Lateral Cuneiform), C (Cuboid), N (Navicular), T (Tarsal) in three different cases is opposite, and the peak von Mises stress of c is greater than that of a and b. The peak von Mises stress of b is between a and c. Conclusion: This study found that a reduced sole-ground contact angle may reduce metatarsal stress fractures. Further, a small sole-ground contact angle may not increase ankle joint injury risk during forefoot running. Hence, given the specialized nature of the running shoes designed for forefoot runners, it is plausible that this study may offer novel insights to guide their athletic pursuits.

A new method proposed for realizing human gait pattern recognition: Inspirations for the application of sports and clinical gait analysis.

BACKGROUND: Finding the best subset of gait features among biomechanical variables is considered very important because of its ability to identify relevant sports and clinical gait pattern differences to be explored under specific study conditions. This study proposes a new method of metaheuristic optimization-based selection of optimal gait features, and then investigates how much contribution the selected gait features can achieve in gait pattern recognition. METHODS: Firstly, 800 group gait datasets performed feature extraction to initially eliminate redundant variables. Then, the metaheuristic optimization algorithm model was performed to select the optimal gait feature, and four classification algorithm models were used to recognize the selected gait feature. Meanwhile, the accuracy results were compared with two widely used feature selection methods and previous studies to verify the validity of the new method. Finally, the final selected features were used to reconstruct the data waveform to interpret the biomechanical meaning of the gait feature. RESULTS: The new method finalized 10 optimal gait features (6 ankle-related and 4-related knee features) based on the extracted 36 gait features (85 % variable explanation) by feature extraction. The accuracy in gait pattern recognition among the optimal gait features selected by the new method (99.81 % ± 0.53 %) was significantly higher than that of the feature-based sorting of effect size (94.69 % ± 2.68 %), the sequential forward selection (95.59 % ± 2.38 %), and the results of previous study. The interval between reconstructed waveform-high and reconstructed waveform-low curves based on the selected feature was larger during the whole stance phase. SIGNIFICANCE: The selected gait feature based on the proposed new method (metaheuristic optimization-based selection) has a great contribution to gait pattern recognition. Sports and clinical gait pattern recognition can benefit from population-based metaheuristic optimization techniques. The metaheuristic optimization algorithms are expected to provide a practical and elegant solution for sports and clinical biomechanical feature selection with better economy and accuracy.

Wide Remote-Range and Accurate Wireless LC Temperature-Humidity Sensor Enabled by Efficient Mutual Interference Mitigation.

Inductor-capacitor wireless integrated sensors (LCWISs) featuring untethered and multitarget measurements are promising in health monitoring and human-machine interfaces. However, the lack of a profound understanding of the internal interference hinders the design of the LCWIS, which has a wide remote sensing range and high accuracy. Herein, a mutually exclusive effect of the mutual inductance interferences in LCWIS was revealed and quantified, enabling a design with a wide range of remote sensing (working distance comparable to the single-target device, working radius: 4 mm) and 16% reduced area. As a key to accurate multitarget measurement, a quantified target interference model based on interference decomposition was proposed to understand the target interferences, providing profound guidance for the design of ultra-accurate LCWIS. As a proof, we designed a cellulose-polyacrylate-cellulose LCWIS (CPC-LCWIS) with ultrahigh accuracies (∼1.2% RH and ∼0.18 °C) beyond commercial wired gauges. The CPC-LCWIS with full-coil sensing structures achieved exceptionally high sensitivities (0.36 MHz/°C and 0.25 MHz/% RH). The CPC-LCWIS was validated for health monitoring and human-machine interfaces. The concept studied in this work provides profound guidance for designing a high-performance flexible LCWIS for advanced wearable electronics.

Adaptive neuro-fuzzy inference system model driven by the non-negative matrix factorization-extracted muscle synergy patterns to estimate lower limb joint movements.

BACKGROUND AND OBJECTIVE: For patients with movement disorders, the main clinical focus is on exercise rehabilitation to help recover lost motor function, which is achieved by relevant assisted equipment. The basis for seamless control of the assisted equipment is to achieve accurate inference of the user's movement intentions in the human-machine interface. This study proposed a novel movement intention detection technology for estimating lower limb joint continuous kinematic variables following muscle synergy patterns, to develop applications for more efficient assisted rehabilitation training. METHODS: This study recruited 16 healthy males and 16 male patients with symptomatic patellar tendinopathy (VISA-P: 59.1 ± 8.7). The surface electromyography of 12 muscles and lower limb joint kinematic and kinetic data from healthy subjects and patients during step-off landings from 30 cm-high stair steps were collected. We subsequently solved the preprocessed data based on the established recursive model of second-order differential equation to obtain the muscle activation matrix, and then imported it into the non-negative matrix factorization model to obtain the muscle synergy matrix. Finally, the lower limb neuromuscular synergy pattern was then imported into the developed adaptive neuro-fuzzy inference system non-linear regression model to estimate the human movement intention during this movement pattern. RESULTS: Six muscle synergies were determined to construct the muscle synergy pattern driven ANFIS model. Three fuzzy rules were determined in most estimation cases. Combining the results of the four error indicators across the estimated variables indicates that the current model has excellent estimated performance in estimating lower limb joint movement. The estimation errors between the healthy (Angle: R2=0.98±0.03; Torque: R2=0.96±0.04) and patient (Angle: R2=0.98±0.02; Torque: R2=0.96±0.03) groups are consistent. CONCLUSION: The proposed model of this study can accurately and reliably estimate lower limb joint movements, and the effectiveness will also be radiated to the patient group. This revealed that our models also have certain advantages in the recognition of motor intentions in patients with relevant movement disorders. Future work from this study can be focused on sports rehabilitation in the clinical field by achieving more flexible and precise movement control of the lower limb assisted equipment to help the rehabilitation of patients.

Accurately and effectively predict the ACL force: Utilizing biomechanical landing pattern before and after-fatigue.

BACKGROUND AND OBJECTIVE: As a fundamental exercise technique, landing can commonly be associated with anterior cruciate ligament (ACL) injury, especially during after-fatigue single-leg landing (SL). Presently, the inability to accurately detect ACL loading makes it difficult to recognize the risk degree of ACL injury, which reduces the effectiveness of injury prevention and sports monitoring. Increased risk of ACL injury during after-fatigue SL may be related to changes in ankle motion patterns. Therefore, this study aims to develop a highly accurate and easily implemented ACL force prediction model by combining deep learning and the explored relationship between ACL force and ankle motion pattern. METHODS: First, 56 subjects' during before and after-fatigue SL data were collected to explore the relationship between the ankle initial contact angle (AIC), ankle range of motion (AROM) and peak ACL force (PAF). Then, the musculoskeletal model was developed to simulate and calculate the ACL force. Finally, the ACL force prediction model was constructed by combining the explored relationship and sparrow search algorithm (SSA) to optimize the extreme learning machine (ELM) and long short-term memory (LSTM). RESULTS: There was almost a stronger linear relationship between the PAF and AIC (R = -0.70), AROM (R2 = -0.61). By substituting AIC and AROM as independent variables in the SSA-ELM prediction model, the model shows excellent prediction performance because of very strong correlation (R2 = 0.9992,  MSE = 0.0023,  RMSE = 0.0474). Based on the equal scaling by combining results of SSA-ELM and SSA-LSTM, the prediction model achieves excellent performance in ACL force prediction of the overall waveform (R2 = 0.9947,  MSE = 0.0076,  RMSE = 0.0873). CONCLUSION: By increasing the AIC and AROM during SL, the lower limb joint energy dissipation can be increased and the PAF reduced, thus reducing the impact loads on the lower limb joints and reducing ACL injuries. The proposed ACL dynamic load force prediction model has low input variable demands (sagittal joint angles), excellent generalization capabilities and superior performance in terms of high accuracy. In the future, we plan to use it as an accurate ACL injury risk assessment tool to promote and apply it to a wider range of sports training and injury monitoring.

Outstanding Humidity Chemiresistors Based on Imine-Linked Covalent Organic Framework Films for Human Respiration Monitoring.

Human metabolite moisture detection is important in health monitoring and non-invasive diagnosis. However, ultra-sensitive quantitative extraction of respiration information in real-time remains a great challenge. Herein, chemiresistors based on imine-linked covalent organic framework (COF) films with dual-active sites are fabricated to address this issue, which demonstrates an amplified humidity-sensing signal performance. By regulation of monomers and functional groups, these COF films can be pre-engineered to achieve high response, wide detection range, fast response, and recovery time. Under the condition of relative humidity ranging from 13 to 98%, the COFTAPB-DHTA film-based humidity sensor exhibits outstanding humidity sensing performance with an expanded response value of 390 times. Furthermore, the response values of the COF film-based sensor are highly linear to the relative humidity in the range below 60%, reflecting a quantitative sensing mechanism at the molecular level. Based on the dual-site adsorption of the (-C=N-) and (C-N) stretching vibrations, the reversible tautomerism induced by hydrogen bonding with water molecules is demonstrated to be the main intrinsic mechanism for this effective humidity detection. In addition, the synthesized COF films can be further exploited to effectively detect human nasal and oral breathing as well as fabric permeability, which will inspire novel designs for effective humidity-detection devices.

Simulation of Lower Limb Muscle Activation Using Running Shoes with Different Heel-to-Toe Drops Using Opensim.

BACKGROUND: Although numerous studies have been conducted to investigate the acute effects of shoe drops on running kinematics and kinetic variables, their effects on muscle forces remain unknown. Thus, the primary aim of this study was to compare the muscle force, kinematics, and kinetic variables of habitually rearfoot runners with heel-to-toe drops of negative 8 mm shoes (minimalist shoes) and positive 9 mm shoes (normal shoes) during the running stance phase by using musculoskeletal modeling and simulation techniques. METHODS: Experimental data of lower limb kinematics, ground reaction force, and muscle activation from 16 healthy runners with rearfoot strike patterns were collected and analyzed in OpenSim. Using Matlab, the statistical parameter mapping paired t-test was used to compare the joint angle, moment, and muscle force waveform. RESULTS: The results revealed differences in the sagittal ankle and hip angles and sagittal knee moments between the different heel-to-toe drops of running shoes. Specifically, it showed that the negative 8 mm running shoes led to significantly smaller values than the positive 9 mm running shoes in terms of the angle of ankle dorsiflexion, ankle eversion, knee flexion, hip flexion, and hip internal and hip external rotation. The peak ankle dorsiflexion moment, ankle plantarflexion moment, ankle eversion moment, knee flexion moment, knee abduction moment, and knee internal rotation also decreased obviously with the minimalist running shoes, while the lateral gastrocnemius, Achilleas tendon, and extensor hallucis longus muscles were obviously greater in the minimalist shoes compared to normal shoes. The vastus medialis, vastus lateralis and extensor digitorum longus muscles force were smaller in the minimalist shoes. CONCLUSIONS: Runners may shift to a midfoot strike pattern when wearing negative running shoes. High muscle forces in the gastrocnemius lateral, Achilleas tendon, and flexor hallucis longus muscles may also indicate an increased risk of Achilleas tendonitis and ankle flexor injuries.

Fully Flexible MXene-based Gas Sensor on Paper for Highly Sensitive Room-Temperature Nitrogen Dioxide Detection.

Flexible chemiresistive gas sensors have attracted growing interest due to their capability in real-time and rapid detection of gas. However, the performance of gas sensors has long been hindered by the poor charge transfer ability between the conventional metal electrode and gas sensing semiconductors. Herein, for the first time, a fully flexible paper-based gas sensor integrated with the Ti3C2Tx-MXene nonmetallic electrode and the Ti3C2Tx/WS2 gas sensing film was designed to form Ohmic contact and Schottky heterojunction in a single gas sensing channel. Ti3C2Tx/WS2 has outstanding physical and chemical properties for both Ti3C2Tx and WS2 nanoflakes, showing high conductivity, effective charge transfer, and abundant active sites for gas sensing. The response of the gas sensor to NO2 (1 ppm) at room temperature is 15.2%, which is about 3.2 and 76.0 times as high as that of the Au interdigital electrode integrated with the Ti3C2Tx/WS2 sensor (4.8%) and the MXene electrode integrated with the Ti3C2Tx sensor (0.2%), respectively. Besides, this design performed at a limit of detection with 11.0 ppb NO2 gas and displayed excellent stability under high humidities. Based on first-principles density functional theory calculation results, the improvement of the gas sensing performance can be mainly attributed to the heterojunction regulation effect, work function matching, and suppressing metal-induced gap states. This work provides a new approach for the design of flexible gas sensors on paper with MXene-based conductive electrodes and gas sensing materials.

Fast and recoverable NO2 detection achieved by assembling ZnO on Ti3C2Tx MXene nanosheets under UV illumination at room temperature.

Recently, Ti3C2Tx MXenes have begun to receive attention in the field of gas sensors owing to their characteristics of high conductivity and abundant surface functional groups. However, Ti3C2Tx-based gas sensors still suffer from the drawbacks of low sensitivity and sluggish response/recovery speed towards target gases, limiting their development in further applications. In this work, Ti3C2Tx-ZnO nanosheet hybrids were fabricated through a simple sonication method. The Ti3C2Tx-ZnO nanosheet hybrids exhibited a short recovery time (10 s) under UV (ultraviolet) illumination, a short response time (22 s), a high sensitivity (367.63% to 20 ppm NO2) and selectivity. Furthermore, the Ti3C2Tx-ZnO sensor has prominent anti-humidity properties, as well as superior reproducibility in multiple tests. The abundant active sites in the Ti3C2Tx-ZnO nanosheet hybrids, including surface groups (-F, -OH, -O) of Ti3C2Tx and oxygen vacancies of ZnO, the formation of Schottky barriers between Ti3C2Tx and ZnO nanosheets and the rich photogenerated charge carriers of ZnO under UV illumination, together result in excellent gas-sensing performance. Density functional theory calculations have been further employed to explore the sensing performance of Ti3C2Tx and ZnO nanosheets, showing strong interactions existing between the NO2 and ZnO nanosheets. The main adsorption sites for NO2 were present on the ZnO nanosheets, while the Ti3C2Tx played the role of the conductive path to accelerate the transformation of charge carriers. Our work can provide an effective way for improving the gas-sensing performances of Ti3C2Tx-based gas sensors.

Explaining the differences of gait patterns between high and low-mileage runners with machine learning.

Running gait patterns have implications for revealing the causes of injuries between higher-mileage runners and low-mileage runners. However, there is limited research on the possible relationships between running gait patterns and weekly running mileages. In recent years, machine learning algorithms have been used for pattern recognition and classification of gait features to emphasize the uniqueness of gait patterns. However, they all have a representative problem of being a black box that often lacks the interpretability of the predicted results of the classifier. Therefore, this study was conducted using a Deep Neural Network (DNN) model and Layer-wise Relevance Propagation (LRP) technology to investigate the differences in running gait patterns between higher-mileage runners and low-mileage runners. It was found that the ankle and knee provide considerable information to recognize gait features, especially in the sagittal and transverse planes. This may be the reason why high-mileage and low-mileage runners have different injury patterns due to their different gait patterns. The early stages of stance are very important in gait pattern recognition because the pattern contains effective information related to gait. The findings of the study noted that LRP completes a feasible interpretation of the predicted results of the model, thus providing more interesting insights and more effective information for analyzing gait patterns.