ABSTRACT
A multiple self-powered sensor-integrated mobile manipulator (MSIMM) system was proposed to address challenges in existing exploration devices, such as the need for a constant energy supply, limited variety of sensed information, and difficult human-computer interfaces. The MSIMM system integrates triboelectric nanogenerator (TENG)-based self-powered sensors, a bionic manipulator, and wireless gesture control, enhancing sensor data usability through machine learning. Specifically, the system includes a tracked vehicle platform carrying the manipulator and electronics, including a storage battery and a microcontroller unit (MCU). An integrated sensor glove and terminal application (APP) enable intuitive manipulator control, improving human-computer interaction. The system responds to and analyzes various environmental stimuli, including the droplet and fall height, temperature, pressure, material type, angles, angular velocity direction, and acceleration amplitude and direction. The manipulator, fabricated using 3D printing technology, integrates multiple sensors that generate electrical signals through the triboelectric effect of mechanical motion. These signals are classified using convolutional neural networks for accurate environmental monitoring. Our database shows signal recognition and classification accuracy exceeding 94%, with specific accuracies of 100% for pressure sensors, 99.55% for angle sensors, and 98.66, 95.91, 96.27, and 94.13% for material, droplet, temperature, and acceleration sensors, respectively.
ABSTRACT
The biomimetic electronic nose (e-nose) technology is a novel technology used for the identification and monitoring of complex gas molecules, and it is gaining significance in this field. However, due to the complexity and multiplicity of gas mixtures, the accuracy of electronic noses in predicting gas concentrations using traditional regression algorithms is not ideal. This paper presents a solution to the difficulty by introducing a fusion network model that utilizes a transformer-based multikernel feature fusion (TMKFF) module combined with a 1DCNN_LSTM network to enhance the accuracy of regression prediction for gas mixture concentrations using a portable electronic nose. The experimental findings demonstrate that the regression prediction performance of the fusion network is significantly superior to that of single models such as convolutional neural network (CNN) and long short-term memory (LSTM). The present study demonstrates the efficacy of our fusion network model in accurately predicting the concentrations of multiple target gases, such as SO2, NO2, and CO, in a gas mixture. Specifically, our algorithm exhibits substantial benefits in enhancing the prediction performance of low-concentration SO2 gas, which is a noteworthy achievement. The determination coefficient (R2) values of 93, 98, and 99% correspondingly demonstrate that the model is very capable of explaining the variation in the concentration of the target gases. The root-mean-square errors (RMSE) are 0.0760, 0.0711, and 3.3825, respectively, while the mean absolute errors (MAE) are 0.0507, 0.0549, and 2.5874, respectively. These results indicate that the model has relatively small prediction errors. The method we have developed holds significant potential for practical applications in detecting atmospheric pollution detection and other molecular detection areas in complex environments.
Subject(s)
Electronic Nose , Gases , Gases/chemistry , Gases/analysis , Neural Networks, Computer , Algorithms , Sulfur Dioxide/analysis , Artificial IntelligenceABSTRACT
An electronic tongue (E-tongue) comprises a series of sensors that simulate human perception of taste and embedded artificial intelligence (AI) for data analysis and recognition. Traditional E-tongues based on electrochemical methods suffer from a bulky size and require larger sample volumes and extra power sources, limiting their applications in in vivo medical diagnosis and analytical chemistry. Inspired by the mechanics of the human tongue, triboelectric components have been incorporated into E-tongue platforms to overcome these limitations. In this study, an integrated multichannel triboelectric bioinspired E-tongue (TBIET) device was developed on a single glass slide chip to improve the device's taste classification accuracy by utilizing numerous sensory signals. The detection capability of the TBIET was further validated using various test samples, including representative human body, environmental, and beverage samples. The TBIET achieved a remarkably high classification accuracy. For instance, chemical solutions showed 100% identification accuracy, environmental samples reached 98.3% accuracy, and four typical teas demonstrated 97.0% accuracy. Additionally, the classification accuracy of NaCl solutions with five different concentrations reached 96.9%. The innovative TBIET exhibits a remarkable capacity to detect and analyze droplets with ultrahigh sensitivity to their electrical properties. Moreover, it offers a high degree of reliability in accurately detecting and analyzing various liquid samples within a short timeframe. The development of a self-powered portable triboelectric E-tongue prototype is a notable advancement in the field and is one that can greatly enhance the feasibility of rapid on-site detection of liquid samples in various settings.
ABSTRACT
Excessive exposure to airborne particulate matter (PM), especially PM2.5 particles, adversely affects human health. Conventional PM2.5 detection instruments based on light scattering are generally bulky, expensive, and easily affected by particle size and composition. Here, we report a low-cost and compact one-stop PM2.5 detection platform by integrating a three-dimensional (3D) printed virtual impactor with a QCM sensor. To reduce eddy and airflow impact on the side wall and improve the VI lifetime, a computational fluid dynamics simulation is used to optimize the VI structure. Results show that when the included angle between the minor flow channel and the inner side of the major flow channel is 40-45° and the included angle between the inlet channel and the outside wall of the major flow channel is 125°, the VI has a relatively small particle loss, eddy, and good collection efficiency. Finally, the system detection performance is experimentally evaluated with a sensitivity of 0.08 Hz/min per µg/m3, showing a comparable performance with the commercial instrument.
ABSTRACT
Monitoring of intravenous infusion together with an alarm system is significant for safety and automation operation in the process of clinical drug delivery for major medical institutions. However, there is still a lack of multifunctional sensors to monitor the whole infusion process, such as flow rate, drip rate, and temperature. Herein, we propose a self-powered droplet triboelectric sensor (SDTS) based on the principle of liquid-solid triboelectrification to monitor both intravenous infusion flow and infusion type. Such SDTS devices use two materials with different electrically charged properties to directly generate an electrical signal without any additional power supply, which is conducive to the formation of a large-scale detection system and for enhancing the convenience of medical treatment. The SDTS placed in a disposable infusion set has high potential application in clinical practice and is low cost and easy to prepare. Specifically, we demonstrate the feasibility of the detection of the current infusion flow rate and identification of the infusion medicine type according to the triboelectric signals, providing a new solution for real-time monitoring of patient infusion in nursing wards.
Subject(s)
Drug Delivery Systems , Electric Power Supplies , Humans , Electricity , TemperatureABSTRACT
Trace biological sample detection is critical for the analysis of pathologies in biomedicine. Integration of microfluidic manipulation techniques typically strengthens biosensing performance. For instance, using isothermal amplification reactions to sense trace miRNA in peripheral circulation lacks a sufficiently complex pretreatment process that limits the sensitivity of on-chip detection. Herein we propose an orthogonal tunable acoustic tweezer (OTAT) to simultaneously actuate the transportation and centrifugation of µ-droplets on a single device. The OTAT enables diversified modes of droplet transportation such as unidirectional transport, multi-direction transport, round-trip transport, tilt angle movement, multi-droplet fusion, and continuous centrifugation of the dynamic droplets simultaneously. The multiplicity of modalities enables the focusing of a loaded analyte at the center of the droplet or constant rotation about the center axis of the droplet. We herein demonstrate the OTAT's ability to actuate transportation, fusion, and centrifugation-based pretreatment of two biological sample droplets loaded with miRNA biomarkers and multiple mixtures, as well as facilitating the increase of fluorescence detection sensitivity by an order of magnitude compared to traditional tube reaction methods. The results herein demonstrate the OTAT-based droplet acoustofluidic platform's ability to combine a wide range of biosensing mechanisms and provide a higher accuracy of detection for one-stop point-of-care disease diagnosis.
Subject(s)
Acoustics , MicroRNAs , Centrifugation , Rotation , BiomarkersABSTRACT
Sessile droplets play a crucial role in the microreactors of biochemical samples. Acoustofluidics provide a non-contact and label-free method for manipulating particles, cells, and chemical analytes in droplets. In the present study, we propose a micro-stirring application based on acoustic swirls in sessile droplets. The acoustic swirls are formed inside the droplets by asymmetric coupling of surface acoustic waves (SAWs). With the merits of the slanted design of the interdigital electrode, the excitation position of SAWs is selective by sweeping in wide frequency ranges, allowing for the droplet position to be customized within the aperture region. We verify the reasonable existence of acoustic swirls in sessile droplets by a combination of simulations and experiments. The different periphery of the droplet meeting with SAWs will produce acoustic streaming phenomena with different intensities. The experiments demonstrate that acoustic swirls formed after SAWs encountering droplet boundaries will be more obvious. The acoustic swirls have strong stirring abilities to rapidly dissolve the yeast cell powder granules. Therefore, acoustic swirls are expected to be an effective means for rapid stirring of biomolecules and chemicals, providing a new approach to micro-stirring in biomedicine and chemistry.
ABSTRACT
Surface acoustic wave (SAW)-based acoustofluidic devices have shown broad applications in microfluidic actuation and particle/cell manipulation. Conventional SAW acoustofluidic device fabrication generally includes photolithography and lift-off processes and thus requires accessing cleanroom facilities and expensive lithography equipment. In this paper, we report a femtosecond laser direct writing mask method for acoustofluidic device preparation. By micromachining of steel foil to form the mask and direct evaporation of metal on the piezoelectric substrate using the mask, the interdigital transducer (IDT) electrodes of the SAW device are generated. The minimum spatial periodicity of the IDT finger is about 200 µm, and the preparation for LiNbO3 and ZnO thin films and flexible PVDF SAW devices is verified. Meanwhile, we have demonstrated various microfluidic functions, including streaming, concentration, pumping, jumping, jetting, nebulization, and particle alignment using the fabricated acoustofluidic (ZnO/Al plate, LiNbO3) devices. Compared to the traditional manufacturing process, the proposed method omits spin coating, drying, lithography, developing, and lift-off processes and thus has advantages of simple, convenient, low cost, and environment friendliness.
ABSTRACT
The ability to weigh microsubstances present in low concentrations is an important tool for environmental monitoring and chemical analysis. For instance, developing a rapid analysis platform that identifies the material type of microplastics in seawater would help evaluate the potential toxicity to marine organisms. In this study, we demonstrate the integration of two different techniques that bring together the functions of sparse particle localization and miniaturized mass sensing on a microelectromechanical system (MEMS) chip for enhanced detection and minimization of negative measurements. The droplet sample for analysis is loaded onto the MEMS chip containing a resonant mass sensor. Through the coupling of a surface acoustic wave (SAW) from a SAW transducer into the chip, the initially dispersed microparticles in the droplet are localized over the detection area of the MEMS sensor, which is only 200 µm wide. The accreted mass of the particles is then calibrated against the resulting shift in resonant frequency of the sensor. The SAW device and MEMS chip are detachable after use, allowing the reuse of the SAW device part of the setup instead of the disposal of both parts. Our platform maintains the strengths of noncontact and label-free dual-chip acoustofluidic devices, demonstrating for the first time an integrated microparticle manipulation and real-time mass measurement platform useful for the analysis of sparse microsubstances.
ABSTRACT
Acoustofluidic platforms for cell manipulation benefit from being contactless and label-free at potentially low cost. Particle concentration in a droplet relies on augmenting spatial asymmetry in the acoustic field, which is difficult to reproduce reliably. Etching periodic patterns into a chip to create acoustic band gaps is an attractive approach to spatially modify the acoustic field. However, the sensitivity of acoustic band structures to geometrical tolerances requires the use of costly microfabrication processes. In this work, we demonstrate particle concentration across a range of periodic structure patterns fabricated with a laser-cutting tool, suitable for low-cost and low-volume rapid prototyping. The relaxation on precision is underscored by experimental results of equally efficient particle concentration outside band gaps and even in their absence, allowing operation over a range of frequencies independent of acoustic band gaps. These results are significant by indicating the potential of extending the proposed method from the microscale (e.g. tumor cells) to the nanoscale (e.g. bacteria) by scaling up the frequency without being limited by fabrication capabilities. We demonstrate the device's high degree of biocompatibility to illustrate the method's applicability in the biomedical field for applications such as basic biochemical analysis and in vitro diagnosis.
Subject(s)
Acoustics , Nanoparticles , Lasers , MicrotechnologyABSTRACT
This work describes a two-chip acoustofluidic platform for two-dimensional (2D) manipulation of microparticles in a closed microchamber on a reusable surface acoustic wave (SAW) device. This platform comprises two microfabricated chips: (1) a detachable silicon superstrate enclosed by a PDMS microfluidic chamber and (2) a reusable SAW device for generating standing SAW (SSAW), which is typically an expensive component. Critical to such a two-chip acoustofluidic platform is the selection of a suitable coupling agent at the interface of the SAW device and superstrate. To this end, we applied a polymer thin film as a coupling agent that balances between acoustic coupling efficiency, stability over time, and reusability. Recycling of the SAW device lowers the cost-barrier for acoustofluidic particle manipulation. The SSAW is transmitted into the silicon superstrate via the coupling agent to form a standing Lamb wave (SLW) to trap and move microparticles. The reported two-chip strategy enables the single-use microfluidic superstrates to avoid chemical and biological contaminations, while maintaining the merits of acoustofluidic manipulation of being noncontact and label-free and applicable to a wide range of microparticles with different shapes, density, polarity, and electrical properties.