Estimation of microtubule-generated forces using a DNA origami nanospring.

Microtubules are dynamic cytoskeletal filaments that can generate forces when polymerizing and depolymerizing. Proteins that follow growing or shortening microtubule ends and couple forces to cargo movement are important for a wide range of cellular processes. Quantifying these forces and the composition of protein complexes at dynamic microtubule ends is challenging and requires sophisticated instrumentation. Here, we present an experimental approach to estimate microtubule-generated forces through the extension of a fluorescent spring-shaped DNA origami molecule. Optical readout of the spring extension enables recording of force production simultaneously with single-molecule fluorescence of proteins getting recruited to the site of force generation. DNA nanosprings enable multiplexing of force measurements and only require a fluorescence microscope and basic laboratory equipment. We validate the performance of DNA nanosprings against results obtained using optical trapping. Finally, we demonstrate the use of the nanospring to study proteins that couple microtubule growth and shortening to force generation.

Scott-Russel Linkage-Based Triboelectric Self-Powered Sensor for Contact Material-Independent Force Sensing and Tactile Recognition.

The rapid growth of Internet of Things (IoT) in recent years has increased demand for various sensors to collect a wide range of data. Among various sensors, the demand for force sensors that can recognize physical phenomena in 3D space has notably increased. Recent research has focused on developing energy harvesting methods for sensors to address their maintenance problems. Triboelectric nanogenerator (TENG) based force sensors are a promising solution for converting external motion into electrical signals. However, conventional TENG-based force sensors that use the signal peak can negatively affect data accuracy. In this study, a Scott-Russell linkage-inspired TENG (SRI-TENG) is developed. The SRI-TENG has completely separate signal generation and measurement sections, and the number of peaks in the electrical output is measured to prevent disturbing output signals. In addition, the lubricant liquid enhances durability, enabling stable force signal measurements for 270 000 cycles. The SRI system demonstrates consistent peak counts and high accuracy across different contacting surfaces, indicating that it can function as a contact material-independent self-powered force sensor. Furthermore, using a deep learning method, it is demonstrated that it can function as a multimodal sensor by realizing the tactile properties of various materials.

The characterization of cell traction force on nonflat surfaces with different curvature by elastic hydrogel microspheres.

It is of great importance to study the detachment/attachment behaviors of cells (cancer cell, immune cell, and epithelial cell), as they are closely related with tumor metastasis, immunoreaction, and tissue development at variety scales. To characterize the detachment/attachment during the interaction between cells and substrate, some researchers proposed using cell traction force (CTF) as the indicator. To date, various strategies have been developed to measure the CTF. However, these methods only realize the measurements of cell passive forces on flat cases. To quantify the active CTF on nonflat surfaces, which can better mimic the in vivo case, we employed elastic hydrogel microspheres as a force sensor. The microspheres were fabricated by microfluidic chips with controllable size and mechanical properties to mimic substrate. Cells were cultured on microsphere and the CTF led to the deformation of microsphere. By detecting the morphology information, the CTF exerted by attached cells can be calculated by the in-house numerical code. Using these microspheres, the CTF of various cells (including tumor cell, immunological cell, and epithelium cell) were successfully obtained on nonflat surfaces with different curvature radii. The proposed method provides a versatile platform to measure the CTF with high precision and to understand the detachment/attachment behaviors during physiology processes.

Clinical determination of the natural distensibility of the human ureter: initial study.

OBJECTIVES: To define the natural distensibility of the human ureter and evaluate the impact of other possibly favourable factors on ureteric distensibility. PATIENTS AND METHODS: A total of 101 patients undergoing ureteroscopic stone removal or percutaneous nephrolithotomy underwent ureteric sizing using sequential passage of 37-cm urethral dilators in 2-F increments while attached to a unique force sensor. Insertion forces were limited to 6 N. After 6 N was attained, an appropriately sized ureteric access sheath was passed. At the conclusion of each procedure, Post-Ureteroscopic Lesion Scale score was determined. RESULTS: Urethral dilators were passed in 61% of patients at ≤14 F; 39% of patients accepted urethral dilators of ≥16 F. The mean dilator size was 14 F. Multivariate logistic regression analysis revealed that preprocedural ureteric stenting and antibiotic use favoured passage of 16-F dilators (odds ratio [OR] 5.16, 95% confidence interval [CI] 1.70-15.62 [P = 0.004] and OR 5.15, 95% CI 1.743-15.243 [P = 0.003], respectively). Neither tamsulosin nor prior urinary tract infection had an impact on ureteric size (OR 0.765, 95% CI 0.281-2.084 [P = 0.601], OR 1.049, 95% CI 0.269-4.089 [P = 0.945], respectively). CONCLUSION: Using continuous insertion force monitoring and a 6-N threshold, the majority of unstented adult human ureters within our patient population safely accommodated a 14-F dilator. Safe passage of a 16-F dilator at the 6-N threshold was more likely among patients with a preexisting indwelling ureteric stent or patients who were treated with antibiotics within a week of the procedure.

Active liquid crystals powered by force-sensing DNA-motor clusters.

Cytoskeletal active nematics exhibit striking nonequilibrium dynamics that are powered by energy-consuming molecular motors. To gain insight into the structure and mechanics of these materials, we design programmable clusters in which kinesin motors are linked by a double-stranded DNA linker. The efficiency by which DNA-based clusters power active nematics depends on both the stepping dynamics of the kinesin motors and the chemical structure of the polymeric linker. Fluorescence anisotropy measurements reveal that the motor clusters, like filamentous microtubules, exhibit local nematic order. The properties of the DNA linker enable the design of force-sensing clusters. When the load across the linker exceeds a critical threshold, the clusters fall apart, ceasing to generate active stresses and slowing the system dynamics. Fluorescence readout reveals the fraction of bound clusters that generate interfilament sliding. In turn, this yields the average load experienced by the kinesin motors as they step along the microtubules. DNA-motor clusters provide a foundation for understanding the molecular mechanism by which nanoscale molecular motors collectively generate mesoscopic active stresses, which in turn power macroscale nonequilibrium dynamics of active nematics.

Flexible Force Sensor Based on a PVA/AgNWs Nanocomposite and Cellulose Acetate.

Nanocomposites are materials of special interest for the development of flexible electronic, optical, and mechanical devices in applications such as transparent conductive electrodes and flexible electronic sensors. These materials take advantage of the electrical, chemical, and mechanical properties of a polymeric matrix, especially in force sensors, as well as the properties of a conductive filler such as silver nanowires (AgNWs). In this work, the fabrication of a force sensor using AgNWs synthesized via the polyol chemical technique is presented. The nanowires were deposited via drop-casting in polyvinyl alcohol (PVA) to form the active (electrode) and resistive (nanocomposite) sensor films, with both films separated by a cellulose acetate substrate. The dimensions of the resulting sensor are 35 mm × 40 mm × 0.1 mm. The sensor shows an applied force ranging from 0 to 3.92 N, with a sensitivity of 0.039 N. The sensor stand-off resistance, exceeding 50 MΩ, indicates a good ability to detect changes in applied force without an external force. Additionally, studies revealed a response time of 10 ms, stabilization of 9 s, and a degree of hysteresis of 1.9%. The voltage response of the sensor under flexion at an angle of 85° was measured, demonstrating its functionality over a prolonged period. The fabricated sensor can be used in applications that require measuring pressure on irregular surfaces or systems with limited space, such as for estimating movement in robot joints.

Piezoelectric Behaviour in Biodegradable Carrageenan and Iron (III) Oxide Based Sensor.

This paper is dedicated to the research of phenomena noticed during tests of biodegradable carrageenan-based force and pressure sensors. Peculiar voltage characteristics were noticed during the impact tests. Therefore, the sensors' responses to impact were researched more thoroughly, defining time-dependent sensor output signals from calibrated energy impact. The research was performed using experimental methods when a free-falling steel ball impacted the sensor material to create relatively definable impact energy. The sensor's output signal, which is analogue voltage, was registered using an oscilloscope and transmitted to the PC for further analysis. The obtained results showed a very interesting outcome, where the sensor, which was intended to be piezoresistive, demonstrated a combination of behaviour typical for galvanic cells and piezoelectric material. It provides a stable DC output that is sensitive to the applied statical pressure, and in case of a sudden impact, like a hit, it demonstrates piezoelectric behaviour with some particular effects, which are described in the paper as proton transfer in the sensor-sensitive material. Such phenomena and sensor design are a matter of further development and research.

Stretch Sensor: Development of Biodegradable Film.

This article presents research on biodegradable stretch sensors produced using biological material. This sensor uses a piezoresistive effect to indicate stretch, which can be used for force measurement. In this work, an attempt was made to develop the composition of a sensitive material and to design a sensor. The biodegradable base was made from a κ-carrageenan compound mixed with Fe2O3 microparticles and glycerol. The influence of the weight fraction and iron oxide microparticles on the tensile strength and Young's modulus was experimentally investigated. Tensile test specimens consisted of 10-25% iron oxide microparticles of various sizes. The results showed that increasing the mass fraction of the reinforcement improved the Young's modulus compared to the pure sample and decreased the elongation percentage. The GF of the developed films varies from 0.67 to 10.47 depending on composition. In this paper, it was shown that the incorporation of appropriate amounts of Fe2O3 microparticles into κ-carrageenan can achieve dramatic improvements in mechanical properties, resulting in elongation of up to 10%. The developed sensors were experimentally tested, and their sensitivity, stability, and range were determined. Finally, conclusions were drawn on the results obtained.

Design and Validation of Single-Axis 3D-Printed Force Sensor Based on Three Nested Flexible Rings.

Force measurement is crucial in numerous engineering applications, while traditional force sensors often face problems such as elevated expenses or significant measurement errors. To tackle this issue, we propose an innovative force sensor employing three nested flexible rings fabricated through 3D additive manufacturing, which detects external forces through the displacement variations of flexible rings. An analytical model on the basis of the minimal energy method is developed to elucidate the force-displacement correlation with nonlinearity. Both FEM simulations and experiments verify the sensor's effectiveness. This sensor has the advantages of low expenses and easy manufacture, indicating promising prospects in a range of applications, including robotics, the automotive industry, and iatrical equipment.

Research on Monitoring Assistive Devices for Rehabilitation of Movement Disorders through Multi-Sensor Analysis Combined with Deep Learning.

This study aims to integrate a convolutional neural network (CNN) and the Random Forest Model into a rehabilitation assessment device to provide a comprehensive gait analysis in the evaluation of movement disorders to help physicians evaluate rehabilitation progress by distinguishing gait characteristics under different walking modes. Equipped with accelerometers and six-axis force sensors, the device monitors body symmetry and upper limb strength during rehabilitation. Data were collected from normal and abnormal walking groups. A knee joint limiter was applied to subjects to simulate different levels of movement disorders. Features were extracted from the collected data and analyzed using a CNN. The overall performance was scored with Random Forest Model weights. Significant differences in average acceleration values between the moderately abnormal (MA) and severely abnormal (SA) groups (without vehicle assistance) were observed (p < 0.05), whereas no significant differences were found between the MA with vehicle assistance (MA-V) and SA with vehicle assistance (SA-V) groups (p > 0.05). Force sensor data showed good concentration in the normal walking group and more scatter in the SA-V group. The CNN and Random Forest Model accurately recognized gait conditions, achieving average accuracies of 88.4% and 92.3%, respectively, proving that the method mentioned above provides more accurate gait evaluations for patients with movement disorders.

Sensors and Sensing Devices Utilizing Electrorheological Fluids and Magnetorheological Materials-A Review.

This paper comprehensively reviews sensors and sensing devices developed or/and proposed so far utilizing two smart materials: electrorheological fluids (ERFs) and magnetorheological materials (MRMs) whose rheological characteristics such as stiffness and damping can be controlled by external stimuli; an electrical voltage for ERFs and a magnetic field for MRMs, respectively. In this review article, the MRMs are classified into magnetorheological fluids (MRF), magnetorheological elastomers (MRE) and magnetorheological plastomers (MRP). To easily understand the history of sensing research using these two smart materials, the order of this review article is organized in a chronological manner of ERF sensors, MRF sensors, MRE sensors and MRP sensors. Among many sensors fabricated from each smart material, one or two sensors or sensing devices are adopted to discuss the sensing configuration, working principle and specifications such as accuracy and sensitivity. Some sensors adopted in this article include force sensors, tactile devices, strain sensors, wearable bending sensors, magnetometers, display devices and flux measurement sensors. After briefly describing what has been reviewed in a conclusion, several challenging future works, which should be undertaken for the practical applications of sensors or/and sensing devices, are discussed in terms of response time and new technologies integrating with artificial intelligence neural networks in which several parameters affecting the sensor signals can be precisely and optimally tuned. It is sure that this review article is very helpful to potential readers who are interested in creative sensors using not only the proposed smart materials but also different types of smart materials such as shape memory alloys and active polymers.

Exploring the Performance of an Artificial Intelligence-Based Load Sensor for Total Knee Replacements.

Using tibial sensors in total knee replacements (TKRs) can enhance patient outcomes and reduce early revision surgeries, benefitting hospitals, the National Health Services (NHS), stakeholders, biomedical companies, surgeons, and patients. Having a sensor that is accurate, precise (over the whole surface), and includes a wide range of loads is important to the success of joint force tracking. This research aims to investigate the accuracy of a novel intraoperative load sensor for use in TKRs. This research used a self-developed load sensor and artificial intelligence (AI). The sensor is compatible with Zimmer's Persona Knee System and adaptable to other knee systems. Accuracy and precision were assessed, comparing medial/lateral compartments inside/outside the sensing area and below/within the training load range. Five points were tested on both sides (medial and lateral), inside and outside of the sensing region, and with a range of loads. The average accuracy of the sensor was 83.41% and 84.63% for the load and location predictions, respectively. The highest accuracy, 99.20%, was recorded from inside the sensing area within the training load values, suggesting that expanding the training load range could enhance overall accuracy. The main outcomes were that (1) the load and location predictions were similar in accuracy and precision (p > 0.05) in both compartments, (2) the accuracy and precision of both predictions inside versus outside of the triangular sensing area were comparable (p > 0.05), and (3) there was a significant difference in the accuracy of load and location predictions (p < 0.05) when the load applied was below the training loading range. The intraoperative load sensor demonstrated good accuracy and precision over the whole surface and over a wide range of load values. Minor improvements to the software could greatly improve the results of the sensor. Having a reliable and robust sensor could greatly improve advancements in all joint surgeries.

Flexible 3D Force Sensor Based on Polymer Nanocomposite for Soft Robotics and Medical Applications.

The three-dimensional (3D) force sensor has become essential in industrial and medical applications. The existing conventional 3D force sensors quantify the three-direction force components at a point of interest or extended contact area. However, they are typically made of rigid, complex structures and expensive materials, making them hard to implement in different soft or fixable industrial and medical applications. In this work, a new flexible 3D force sensor based on polymer nanocomposite (PNC) sensing elements was proposed and tested for its sensitivity to forces in the 3D space. Multi-walled carbon nanotube/polyvinylidene fluoride (MWCNT/PVDF) sensing element films were fabricated using the spray coating technique. The MWCNTs play an essential role in strain sensitivity in the sensing elements. They have been utilized for internal strain measurements of the fixable 3D force sensor's structure in response to 3D forces. The MWCNT/PVDF was selected for its high sensitivity and capability to measure high and low-frequency forces. Four sensing elements were distributed into a cross-beam structure configuration, the most typically used solid 3D force sensor. Then, the sensing elements were inserted between two silicone rubber layers to enhance the sensor's flexibility. The developed sensor was tested under different static and dynamic loading scenarios and exhibited excellent sensitivity and ability to distinguish between tension and compression force directions. The proposed sensor can be implemented in vast applications, including soft robotics and prostheses' internal forces of patients with limb amputations.

Conforming Capacitive Load Cells for Conical Pick Cutters.

In underground coal mining, machine operators put themselves at risk when getting close to the machine or cutting face to observe the process. To improve the safety and efficiency of machine operators, a cutting force sensor is proposed. A linear cutting machine is used to cut two separate coal samples cast in concrete with conical pick cutters to simulate mining with a continuous miner. Linear and neural network regression models are fit using 100 random 70:30 test/train splits. The normal force exceeds 60 kN during the rock-cutting tests, and it is averaged using a low pass filter with a 10 Hertz cutoff frequency. The sensor uses measurements of the resonant frequency of capacitive cells in a steel case to determine cutting forces. When used in the rock-cutting experiments, the sensor conforms to the tooling and the stiffness and sensitivity are increased compared to the initial configuration. The sensor is able to track the normal force on the conical picks with a mean absolute error less than 6 kN and an R2 score greater than 0.60 using linear regression. A small neural network with a second-order polynomial expansion is able to improve this to a mean absolute error of less than 4 kN and an R2 score of around 0.80. Filtering measurements before regression fitting is explored. This type of sensor could allow operators to assess tool wear and material type using objective force measurements while maintaining a greater distance from the cutting interface.

Research on Decoupling Model of Six-Component Force Sensor Based on Artificial Neural Network and Polynomial Regression.

A two-stage decoupling model based on an artificial neural network with polynomial regression is proposed for the six-component force sensor load decoupling problem in the case of multidimensional mixed loading. The six-dimensional load categorization stage model constructed in the first stage combines 63 load category label sets with a deep BP neural network. The six-dimensional load regression stage model was constructed by combining polynomial regression with a BP neural network in the second stage. Meanwhile, the six-component force sensor with a fiber Bragg grating (FBG) sensor as the sensitive element was designed, and the elastomer simulation and calibration experimental dataset was established to realize the validation of the two-stage decoupling model. The results based on the simulation data show that the accuracy of the classification stage is 93.65%. The MAPE for the force channel in the regression stage is 6.29%, and 3.24% for the moment channel. The results based on experimental data show that the accuracy of the classification stage is 87.80%. The MAPE for the force channel in the regression phase is 5.63%, and 4.82% for the moment channel.

Study on fabrication of force transducer based on carbon nano-flake balls.

The purpose of this study was to fabricate a force sensor. A novel three-dimensional carbon-based material called a carbon nano-flake ball (CNFB) was used because it exhibits a large surface-area and high electrical conductivity. Moreover, CNFB can be easily fabricated using a one-step process via microwave plasma chemical vapor deposition. In the present study, two different methods, chemical and mechanical exfoliation, were used to fabricate the CNFB thin films. CNFEs were successfully synthesized on the silicon-based composite substrate. The substrate was constructed by the Si, SiO2, and Al2O3, where Al2O3played the role of the substrate for the force sensor while SiO2was the interface layer and was removed in the process by hydrogen fluoride (HF) solution to separate Al2O3from Silicon. The experiments showed that using sol-gel catalyst coating as pretreatment precursor, results in a larger ball-size but lower deposition density of CNFB on Al2O3substrate. By using mechanical exfoliation by polyimide (PI) tape, the CNFB grown on silicon substrate can be easily exfoliated from the substrate. PI/CNFB was successfully exfoliated from the substrate with a silver-grey color at the bottom of the CNFB which is likely to be silicon carbide (SiC) from the energy dispersive spectrometer analysis. The sheet resistance of PI/CNFB was 18.3 ± 1.0 Ω sq.-1PI/CNFB exhibits a good force sensing performance with good stability after 10 times of loading-unloading cycles and a good sensitivity of 11.6 Ω g-1.

Reproducibility of laryngeal force sensor measurements.

OBJECTIVE: The laryngeal force sensor (LFS) measures force during suspension microlaryngoscopy (SML) procedures, and has been previously shown to predict postoperative complications. Reproducibility of its measurements has not been described. STUDY DESIGN: Prospective cohort study. SETTING: Academic medical center. METHODS: 291 adult patients had force data collected from 2017 to 2021 during various SML procedures. 94 patients had passive LFS monitoring (surgeon blinded to intraoperative recordings) and 197 had active LFS monitoring (surgeon able to see LFS recordings). 27 of these patients had repeat procedures, with unique LFS metrics for each procedure. The 27 patients were divided into three groups. Group 1 had passive use for both procedures, group 2 had passive use for the first procedure and active use for the second, and group 3 had active use for both procedures. Force metrics from the two procedures were compared with a paired samples t-test. RESULTS: For airway dilation procedures and cancer resection procedures, average force variances were significantly lower with active versus passive use of the LFS. Group 1-no significant changes in maximum force (procedure 1 = 163.8 N, procedure 2 = 133.8 N, p = 0.324) or average force (procedure 1 = 93.6 N, procedure 2 = 78.3 N, p = 0.617). Group 2-maximum force dropped by 35 % between procedures 1 (219.2 N) and 2 (142.5 N), p = 0.013. Average force dropped by 42.5 % between procedures 1 (147.2 N) and 2 (84.6 N), p = 0.007. Group 3-no significant changes in maximum force (procedure 1 = 158.6 N, procedure 2 = 158.2 N, p = 0.986) or average force (procedure 1 = 94.2, procedure 2 = 81.8, p = 0.419). CONCLUSIONS: LFS measurements were reproducible for similar procedures in the same patient when the type of LFS monitoring was not a confounder.

Design and Experimental Results of a Three-Dimensional Force Sensor for Shearer Cutting Pick Force Monitoring.

The main focus of this work is the design and development of a three-dimensional force sensor for the cutting pick of a coal mining shearer's simulated drum. This sensor is capable of simultaneously measuring the magnitude of force along three directions of the cutting pick during the cutting sample process. The three-dimensional force sensor is built based on the strain theory of material mechanics, and reasonable structural design is implemented to improve its sensitivity and reduce inter-axis coupling errors. The strain distribution of the sensor is analyzed using finite element analysis software, and the distribution of the strain gauges is determined based on the analysis results. In addition, a calibration test system is designed for the sensor, and the sensitivity, linearity, and inter-axis coupling errors of the sensor are calibrated and tested using loading experiments in three mutually perpendicular directions. Modal simulation analysis and actual cutting pick testing of the coal mining machine's simulated drum are conducted to study the dynamic characteristics and functionality of the sensor in practical applications. The experimental results depict sensitivities of 0.748 mV/V, 2.367 mV/V, and 2.83 mV/V for the newly developed sensor, respectively. Furthermore, the cross-sensitivity error was lower than 5.02%. These findings validate that the sensor's structure satisfies the measurement requirements for pick-cutting forces.

Research of a Cross-Interference Suppression Method for Piezoresistive Three-Dimensional Force Sensor.

Cross-interference is not only an important factor that affects the measuring accuracy of three-dimensional force sensors, but also a technical difficulty in three-dimensional force sensor design. In this paper, a cross-interference suppression method is proposed, based on the octagonal ring's structural symmetry as well as Wheatstone bridge's balance principle. Then, three-dimensional force sensors are developed and tested to verify the feasibility of the proposed method. Experimental results show that the proposed method is effective in cross-interference suppression, and the optimal cross-interference error of the developed sensors is 1.03%. By optimizing the positioning error, angle deviation, and bonding process of strain gauges, the cross-interference error of the sensor can be further reduced to -0.36%.

High-Precision Tribometer for Studies of Adhesive Contacts.

Herein, we describe the design of a laboratory setup operating as a high-precision tribometer. The whole design procedure is presented, starting with a concept, followed by the creation of an exact 3D model and final assembly of all functional parts. The functional idea of the setup is based on a previously designed device that was used to perform more simple tasks. A series of experiments revealed certain disadvantages of the initial setup, for which pertinent solutions were found and implemented. Processing and correction of the data obtained from the device are demonstrated with an example involving backlash and signal drift errors. Correction of both linear and non-linear signal drift errors is considered. We also show that, depending on the research interests, the developed equipment can be further modified by alternating its peripheral parts without changing the main frame of the device.