Signal Collection Research Articles

Active noise control of refrigerator based on cascaded notch feedback algorithm

Refrigerators bring convenience to people, but the noise they produce can also affect people’s lives. Refrigerator noise is dominated by low-frequency noise, and the active noise control method is more effective for this kind of noise. However, the existing active noise control methods for refrigerators do not take into account factors such as the limited space and complex structure of the refrigerator compressor room, and external interference signals will be introduced to affect the noise reduction performance during the error signal collection. Given that, this paper proposes the cascade notch feedback algorithm, which can well reduce the influence of external interference signals to better achieve the refrigerator noise reduction. The algorithm consists of the cascaded notch filtering algorithm and the feedback algorithm. The cascade notch filtering algorithm is formed by cascading the adaptive notch filtering algorithm, which is used to deal with the main frequency and harmonic noise generated by the refrigerator. The feedback algorithm consists of a robust algorithm for dealing with external interference signals introduced during error signal collection. The simulation experiment proves that the algorithm has advantages in terms of noise reduction and computational cost compared with the adaptive notch filtering algorithm and the improved algorithm. The experimental test platform is set up to carry out the actual refrigerator noise reduction experiments under different conditions. The algorithm has the effect of noise reduction under different robust algorithms.

Quantification of Size-Binned Particulate Matter in Electronic Cigarette Aerosols Using Multi-Spectral Optical Sensing and Machine Learning

To monitor health risks associated with vaping, we introduce a multi-spectral optical sensor powered by machine learning for real-time characterization of electronic cigarette aerosols. The sensor can accurately measure the mass of particulate matter (PM) in specific particle size channels, providing essential information for estimating lung deposition of vaping aerosols. For the sensor’s input, wavelength-specific optical attenuation signals are acquired for three separate wavelengths in the ultraviolet, red, and near-infrared range, and the inhalation pressure is collected from a pressure sensor. The sensor’s outputs are PM mass in three size bins, specified as 100–300 nm, 300–600 nm, and 600–1000 nm. Reference measurements of electronic cigarette aerosols, obtained using a custom vaping machine and a scanning mobility particle sizer, provided the ground truth for size-binned PM mass. A lightweight two-layer feedforward neural network was trained using datasets acquired from a wide range of puffing conditions. The performance of the neural network was tested using unseen data collected using new combinations of puffing conditions. The model-predicted values matched closely with the ground truth, and the accuracy reached 81–87% for PM mass in three size bins. Given the sensor’s straightforward optical configuration and the direct collection of signals from undiluted vaping aerosols, the achieved accuracy is notably significant and sufficiently reliable for point-of-interest sensing of vaping aerosols. To the best of our knowledge, this work represents the first instance where machine learning has been applied to directly characterize high-concentration undiluted electronic cigarette aerosols. Our sensor holds great promise in tracking electronic cigarette users’ puff topography with quantification of size-binned PM mass, to support long-term personalized health and wellness.

Multilayer Bionic Tunable Strain Sensor with Mutually Non‐Interfering Conductive Networks for Machine Learning‐Assisted Gesture Recognition

AbstractFlexible strain sensors capable of acquiring tiny mechanical signals and attaching to various irregular surfaces are becoming prevalent in physiological measurement, soft robotics, and human‐machine interaction. However, the advancement of flexible strain sensors is substantially impeded by the intrinsic compromise between sensitivity and the range of detectable signals. Inspired by the multilayer structure of nacre in nature, a carbon nanotubes (CNTs)/graphene (GR)/graphene (GR)/thermoplastic polyurethane (TPU) mat (CGGTM) is prepared by electrospinning and high‐pressure spraying technology. By designing a multilayer structure with mutually non‐interfering conductive networks, the resulting CGGTM possesses a low detection limit (0.05% strain), high sensitivity (gauge factor, GF > 152537), large detection range (up to 364% strain), fast response/recovery time (80 ms/100 ms), and excellent cyclic durability (up to 1000 cycles). Furthermore, the CGGTM also exhibits satisfactory triboelectric performances when assembled into a triboelectric nanogenerator (TENG, 3 × 3 cm2), including high triboelectric output (open‐circuit voltage Voc = 135.4 V, short‐circuit current Isc = 1.25 µA) and power density (88 mW m−2), enabling reliable power supply, self‐powered sensing, and pulse monitoring capability. Finally, CGGTM is successfully applied to the collection of biological signals and multi‐gesture motion recognition assisted by machine learning algorithms, which holds promise for intelligent interaction with gestures in the future.

A novel device for electrocardiographic imaging

Abstract Background Electrocardiographic imaging (ECGi) is a non-invasive imaging tool that allows 3D visualization and characterization of the electrical activity of the heart from measurements on the body surface by solving the so called "inverse problem of electrocardiography". This method helps overcome the limitations of the conventional12-lead ECG in diagnosing and locating heart arrythmias. We developed a textile 34-electrode imageless device (with no need for MRI or CT-scan). Purpose We aim to evaluate the feasibility and safety of the use of a novel textile imageless 34-electrode device for recording ECGi for identification of electrical activity of the heart. Methods and results The performance of a prototype data acquisition system (DAS) was evaluated in 158 individuals. Tests were conducted in three hospitals. The mean age was 43.32 years old (±17.44); mean height 167.31cm (±18.33); mean weight 70kg (±15.68). Almost 88% of the patients were in sinus rhythm. The relative frequencies of the targeted pathologies are listed in Figure 1. The collection of cardiac signals and posterior representation in virtual reality of a stereoscopic, holographic 3D-360º map, was achieved in 100% of the patients. Figure 2 shows the textile hardware with the silver dry electrodes and a screenshot of the software with an example of a voltage map in a patient in sinus rhythm. There were no adverse effects reported. The DAS is composed of 34 electrodes, being their distribution representative of the electrical activity on the torso. Several studies have discussed the minimum number of electrodes on the torso of the patient required to characterize the heart potentials from the electrical potentials on the torso surface through the inverse problem (1,2). We highlight that the representativeness condition of the electrical activity on the torso is crucial to appropriately estimate cardiac activity with the minimum number of leads on the DAS. Conclusion The use of a novel textile imageless 34-electrode device for recording ECGi for identification of electrical activity of the heart is feasible and safe.Targeted rhythmHardware and software

Cellulose-based Conductive Materials for Bioelectronics.

The growing demand for electronic devices has led to excessive stress on Earth's resources, necessitating effective waste management and the search for renewable materials with minimal environmental impact. Bioelectronics, designed to interface with the human body, have traditionally been made from inorganic materials, such as metals, which, while having suitable electrical conductivity, differ significantly in chemical and mechanical properties from biological tissues. This can cause issues such as unreliable signal collection and inflammatory responses. Recently, natural biopolymers such as cellulose, chitosan, and silk have been explored for flexible devices, given their chemical uniqueness, shape flexibility, ease of processing, mechanical strength, and biodegradability. Cellulose is the most abundant natural biopolymer, has been widely used across industries, and can be transformed into electronically conductive carbon materials. This review focuses on the advancements in cellulose-based conductive materials for bioelectronics, detailing their chemical properties, methods to enhance conductivity, and forms used in bioelectronic applications. It highlights the compatibility of cellulose with biological tissues, emphasizing its potential in developing wearable sensors, supercapacitors, and other healthcare-related devices. The review also addresses current challenges in this field and suggests future research directions to overcome these obstacles and fully realize the potential of cellulose-based bioelectronics.

A fast and stable calibration method for VLP system without any geometric measurement

Abstract. Visible light positioning (VLP) is one of the most promising technologies for providing high-precision, low-cost indoor positioning and navigation services. However, the traditional calibration methods of VLP are complex and unfriendly to users, which hinders the large-scale commercial deployment of VLP systems. In this letter, a fast and stable calibration method for Lambert model in the received signal strength (RSS)-based VLP system is proposed, which dispenses with geometric measurement and greatly simplifies the calibration procedures. The proposed method calibrates the Lambert model through two steps, firstly using a ratio method to calibrate the Lambert order, and then estimating the constant term. The actual processes only require moving a robot equipped with a photo-detector (PD) along a rectangular trajectory once, and then the program will automatically estimate the required parameters by analyzing the RSS during this period. Experimental results show a good stability of the calibrated parameters, as well as a excellent distance measuring accuracy within 12 cm. And the entire processes, including signal collection and processing, only take a total of no more than four minutes.

Super-Tough, Non-Swelling Zwitterionic Hydrogel Sensor Based on the Hofmeister Effect for Potential Motion Monitoring of Marine Animals.

Hydrogel-based electronic devices in aquatic environments have sparked widespread research interest. Nevertheless, the challenge of developing hydrogel electronics underwater has not been profoundly surmounted because of the fragility and swelling of hydrogels in aquatic environments. In this work, a zwitterionic double network hydrogel comprisedof polyvinyl alcohol (PVA), poly(sulfobetaine methacrylate) (PSBMA), and sulfuric acid (H2SO4) demonstrates super-tough and non-swelling performance. The Hofmeister effect of H2SO4 and PSBMA induces the PVA chains to form numerous nanocrystalline domains, which serve as the primary physical crosslinking points and provide effective energy dissipation. H2SO4 induces a strong salting-out effect to facilitate PVA crystallization and the formation of a dense and stable network structure that inhibits swelling. The resulting hydrogel exhibits an ultra-high toughness of 4.61MJm-3, non-swelling, and long-term stability for up to a month in pure water and seawater. Based on this, a hydrogel-based seawater strain sensor has been developed to monitor the underwater movements of marine animal models. Reliable and stable sensing performance ensures real-time collection of underwater motion signals, despite the impacts of water flow and the interference of ions. This study provides a facile approach to designing super-tough and non-swelling hydrogels and further expands the application of underwater electronic devices.

A bio-feedback-mimicking electrode combining real-time monitoring and drug delivery

Effective disease management based on real-time physiological changes presents a significant clinical challenge. A flexible electrode system integrating diagnosis and treatment can overcome the uncertainties associated with treatment progress during localized interventions. In this study, we develop a system featuring a biomimetic feedback regulation mechanism for drug delivery and real-time monitoring. To prevent drug leakage, the system incorporates a magnesium (Mg) valve in the outer layer, ensuring zero leakage when drug release is not required. The middle layer contains a drug-laden poly(3,4-ethylenedioxythiophene) (PEDOT) sponge (P-sponge), which supplies the water to partially or fully activate the Mg valve under electrical stimulation and initiate drug release. Once the valve is fully opened, the exposed and expanded P-sponge electrode establishes excellent contact with various tissues, facilitating the collection of electrophysiological signals. Encapsulation with polylactic acid film ensures the system's flexibility and bioresorbability, thereby minimizing potential side effects on surrounding tissues. Animal experiments demonstrate the system's capability to mimic feedback modulation mechanisms, enabling real-time monitoring and timely drug administration. This integrated diagnosis and treatment system offers an effective solution for the emergency management of acute diseases in clinical settings.

A Systematic Investigation Based on BCI and EEG Implemented using Machine Learning Algorithms

BCI is a strong tool that improves human-system communication. It improves the brain's ability to interact with its surroundings. Recent decades have seen substantial advances in neuroscience and computer science. This has made BCI a leader in computational neuroscience and intelligence research. Recent technological advances including wearable sensing devices, real-time data streaming, machine learning, and deep learning have raised the need for electroencephalographic (EEG)-based brain-computer interface (BCI) in clinical and translational applications. EEG-based Brain-Computer Interfaces (BCIs) detect cognitive state variations throughout laborious tasks, making them advantageous for individuals. To fill in the gaps in the wide overview of the past five years (2019-2024), we surveyed the newest research on EEG signal detection and computational intelligence in brain-computer interfaces. To provide a more accurate account, we will begin by reviewing Brain-Computer Interface (BCI) technology and its main challenges. Modern signal detection and enhancement techniques for EEG signal collection and refinement follow. We also provide advanced computational intelligence methods for tracking, maintaining, and monitoring human cognitive and operational performance in everyday applications. Combinations, interpretable fuzzy models, transfer learning, and deep learning are used. We conclude with a sample of cutting-edge BCI-driven healthcare applications and explore future EEG-based BCI research.

MRNet: rolling bearing fault diagnosis in noisy environment based on multi-scale residual convolutional network

Abstract Vibration signal collection of rolling bearings in the complex working environment often suffers from significant noise interference, rendering traditional fault diagnosis methods ineffective. To address this challenge, we propose a multi-scale residual convolutional network (MRNet) for diagnosing rolling bearing faults in noisy environments. The MRNet model features multiple convolution branches, each of which utilizes kernels with different sizes to capture fault information at different scales, so this multi-scale framework excels at extracting both local and global information from raw fault vibration signals, enhancing fault recognition accuracy. Additionally, we introduce residual blocks to maintain global information during the convolution operations, preventing useful feature information loss. To further improve global feature extraction capability of the network model, a lightweight Transformer module is developed and incorporated, compensating for some global information that the network’s front-end might fail to capture. The effectiveness of MRNet is validated by using two publicly available rolling bearing fault datasets and our own experiment dataset. The verification results indicate that MRNet outperforms other comparative models, particularly for complex fault diagnosis in noisy environments.

Fluorescent aptasensor based on competitive recognition strategies and point-of-care testing system for brain natriuretic peptide detection

Brain natriuretic peptide (BNP) is the preferred biomarker for clinical analysis, widely used in early screening and prognostic monitoring of cardiovascular and neurological diseases. Due to the low concentration and short half-life of BNP in the blood, its sensitive detection remains a challenge that must be overcome. With the rapid development of molecular diagnosis and point-of-care testing (POCT), developing a BNP biosensor with high sensitivity, good stability, accuracy, speed, and low-cost is of great significance for clinical applications and emergency diagnosis. However, BNP in human blood exists in various forms, and traditional fluorescence sensors may lead to overestimation of the concentration. In this work, we constructed an “on” fluorescent aptasensor based on competitive recognition strategy for quantitative detection of BNP, and built a fluorescence sensing detection system based on smartphone to achieve rapid on-site detection. We designed a specific aptamer labeled with carboxyfluorescein (FAM) with a simple structure and high biological affinity for selective capture of BNP, and utilized the large specific surface area and excellent fluorescence quenching effect of carboxylated graphene oxide (GO-COOH) as a carrier and quencher for the aptasensor. By optimizing experimental parameters such as aptamer and GO-COOH concentrations, as well as quenching/recovery time, linear sensitive detection of BNP was achieved in the concentration range of 0.01 ng/mL–10 ng/mL, with a detection limit as low as 10 pg/mL, good specificity, and excellent application potential in artificial serum spiking experiments. Additionally, a stable and sensitive fluorescence signal collection system has been developed to meet the needs of portable detection, breaking through the usage environment of traditional fluorescence detection equipment and filling the gap of portable fluorescence biosensing. In summary, we have creatively proposed a fluorescent aptasensor that enables rapid and sensitive detection of BNP and the development of low-cost fluorescent biosensors. The combination with POCT has shown broad application prospects and also provides some ideas for the fluorescence detection of other protein biomarkers.

High-Sensitivity and In Situ Multi-Component Detection of Gases Based on Multiple-Reflection-Cavity-Enhanced Raman Spectroscopy.

Raman spectroscopy with the advantages of the in situ and simultaneous detection of multi-components has been widely used in the identification and quantitative detection of gas. As a type of scattering spectroscopy, the detection sensitivity of Raman spectroscopy is relatively lower, mainly due to the low signal collection efficiency. This paper presents the design and assembly of a multi-channel cavity-enhanced Raman spectroscopy system, optimizing the structure of the sample pool to reduce the loss of the laser and increase the excitation intensity of the Raman signals. Moreover, three channels are used to collect Raman signals to increase the signal collection efficiency for improving the detection sensitivity. The results showed that the limits of detection for the CH4, H2, CO2, O2, and N2 gases were calculated to be 3.1, 34.9, 17.9, 27, and 35.2 ppm, respectively. The established calibration curves showed that the correlation coefficients were all greater than 0.999, indicating an excellent linear correlation and high level of reliability. Meanwhile, under long-time integration detection, the Raman signals of CH4, H2, and CO2 could be clearly distinguished at the concentrations of 10, 10, and 50 ppm, respectively. The results indicated that the designed Raman system possesses broad application prospects in complex field environments.

Sandwich-structured electrospun polyvinylidene difluoride sensor for structural health monitoring of glass fiber reinforced polymer composites

Stress monitoring and interlaminar failure detection attract much attention in glass fiber reinforced polymer (GFRP) structural health monitoring area. However, due to limitations of sensing or electrode materials, existing embedding sensors cannot be designed to have both of the abilities without sacrificing mechanical properties. This work fabricated a sandwich-structured sensor, which is composed of three layers of electrospun polyvinylidene difluoride membranes. The upper and lower electrodes are flexible membranes that ensure stable signal collection in the rigid GFRP material system. The sensor can quantitatively monitor the stress based on piezoelectric effect, and interlaminar crack propagation based on parallel plate capacitors. The voltage and capacitance values have a linear relationship with the stress level and crack length (R-square of the functions is 0.999 and 0.933), respectively. Due to the porous microstructure of electrospun membranes, polymer matric can well infiltrate the polyvinylidene difluoride nanofibers while preparing the sensor embedded GFRP. Thus, the perfect bonding of the sensor within GFRP ensures the effective sensing abilities until sample failure and negligible effect (< -7%) on the mechanical properties of GFRP.

The efficient method to get better raw brain signal on rat anesthetics experiment

This paper introduces an efficient methodology for conducting rat anesthesia experiments, aimed at enhancing the quality of raw brain signals obtained. The proposed approach enables the acquisition of animal brain signals during experiments without the confounding influence of muscle noise. Initially, the use of alpha-chloralose (a-c) in conjunction with Isoflurane is introduced to induce anesthesia in rats. Subsequently, Dexdomitor is administered to prevent muscular movements during the collection of brain signals, further refining the signal quality. Experimental outcomes conclusively demonstrate that our anesthesia method produces cleaner raw signals and exhibits improved robustness during data acquisition, outperforming existing methods that rely solely on Isoflurane or the Ketamine-Xylazine combination. Notably, this improved performance is achieved with minimal alterations to vital physiological parameters, including body temperature, respiration, and heart rates. Moreover, the efficacy of a-c in maintaining anesthesia for up to 7 h stands in contrast to the shorter durations achievable with continuous Isoflurane administration or the 30-min window offered by Ketamine-Xylazine, highlighting the practical advantages of our proposed method. Finally, post-experiment observations confirmed that the animals gradually returned to normal behavior without any signs of distress or adverse effects, indicating that our method was both effective and safe.

A Universal Biocompatible and Multifunctional Solid Electrolyte in p-Type and n-Type Organic Electrochemical Transistors for Complementary Circuits and Bioelectronic Interfaces.

The development of soft and flexible devices for collection of bioelectrical signals is gaining momentum for wearable and implantable applications. Among these devices, organic electrochemical transistors (OECTs) stand out due to their low operating voltage and large signal amplification capable of transducing weak biological signals. While liquid electrolytes have demonstrated efficacy in OECTs, they limit its operating temperature and pose challenges for electronic packaging due to potential leakage. Conversely, solid electrolytes offer advantages such as mechanical flexibility, robustness against environmental factors, and ability to bridge the interface between rigid dry electronics systems and soft wet biological tissues. However, few systems have demonstrated generality and compatibility with a wide range of state-of-the-art organic mixed ionic-electronic conductors (OMIECs). This paper introduces a highly stretchable, flexible, biocompatible, self-healable gelatin-based solid-state electrolyte, compatible with both p- and n-type OMIEC channels while maintaining high performance and excellent stability. Furthermore, this nonvolatile electrolyte is stable up to 120°C and exhibits high ionic conductivity even in dry environment. Additionally, an OECT-based complementary inverter with a record-high normalized-gain of 228 V-1 and a corresponding ultralow static power consumption of 1 nW is demonstrated. These advancements pave the way for versatile applications ranging from bioelectronics to power-efficient implants.

Evolution characteristics of an induced electric charge signal and identification method of charge for instability failure in loaded coal

A coal-loaded charge induction monitoring system is developed to effectively forecast the dynamic disasters caused by coal failure. Specifically, a digital finite impulse response (FIR) filter is designed to denoise and filter the signal, and the time-frequency domain evolution of induced charge signals is analyzed during coal failure experiments. The quantitative relationships between the induced electric charge and stress-strain energy, and ultimately, between induced electric charge and coal deformation/failure, are revealed. Ultimately, the electric charge sensor exhibits high signal collection frequency and high sensitivity, and the FIR low-pass filter constructed in MATLAB effectively denoises and filters induced charge signals. The main frequency range of the white noise is 50–500 Hz, and the main frequency of the charge signal induced by coal deformation and failure is concentrated in the range of 0–50 Hz. The optimal distances for monitoring cubic and cylindrical raw coal samples using this sensor are 9 mm and 11 mm, respectively. Notably, strain energy is released faster when it can dissipate more readily, and induced charge pulses become denser when more intense signals produce large fluctuations. A method is proposed to identify coal deformation and failure based on changes in the induced electric charge. This study provides a new means of monitoring the early warning signs of dynamic coal mine disasters. Based on our experimental results and conclusions, a new method is proposed to identify coal deformation and failure based on changes in the induced electric charge. The precursor to the moment of coal failure can be identified by monitoring the amplitude of the induced charge, the dynamic trend of fluctuation, and the cumulative number of induced electric charge pulses during the process of coal deformation.

Balance confidence classification in people with a lower limb amputation using six minute walk test smartphone sensor signals.

The activities-specific balance confidence scale (ABC) assesses balance confidence during common activities. While low balance confidence can result in activity avoidance, excess confidence can increase fall risk. People with lower limb amputations can present with inconsistent gait, adversely affecting their balance confidence. Previous research demonstrated that clinical outcomes in this population (e.g., stride parameters, fall risk) can be determined from smartphone signals collected during walk tests, but this has not been evaluated for balance confidence. Fifty-eight (58) individuals with lower limb amputation completed a six-minute walk test (6MWT) while a smartphone at the posterior pelvis was used for signal collection. Participant ABC scores were categorized as low confidence or high confidence. A random forest classified ABC groups using features from each step, calculated from smartphone signals. The random forest correctly classified the confidence level of 47 of 58 participants (accuracy 81.0%, sensitivity 63.2%, specificity 89.7%). This research demonstrated that smartphone signal data can classify people with lower limb amputations into balance confidence groups after completing a 6MWT. Integration of this model into the TOHRC Walk Test app would provide balance confidence classification, in addition to previously demonstrated clinical outcomes, after completing a single assessment and could inform individualized rehabilitation programs to improve confidence and prevent activity avoidance.

Materials, Structure, and Interface of Stretchable Interconnects for Wearable Bioelectronics.

Since wearable technologies for telemedicine have emerged to tackle global health concerns, the demand for well-attested wearable healthcare devices with high user comfort also arises. Skin-wearables for health monitoring require mechanical flexibility and stretchability for not only high compatibility with the skin's dynamic nature but also a robust collection of fine health signals from within. Stretchable electrical interconnects, which determine the device's overall integrity, are one of the fundamental units being understated in wearable bioelectronics. In this review, a broad class of materials and engineering methodologies recently researched and developed are presented, and their respective attributes, limitations, and opportunities in designing stretchable interconnects for wearable bioelectronics are offered. Specifically, the electrical and mechanical characteristics of various materials (metals, polymers, carbons, and their composites) are highlighted, along with their compatibility with diverse geometric configurations. Detailed insights into fabrication techniques that are compatible with soft substrates are also provided. Importantly, successful examples of establishing reliable interfacial connections between soft and rigid elements using novel interconnects are reviewed. Lastly, some perspectives and prospects of remaining research challenges and potential pathways for practical utilization of interconnects in wearables are laid out.

Advancing Wearable Cardiovascular Monitoring: Integration of Flexible Piezoresistive Sensors for Robust Pulse Waveform Detection

Wearable, flexible piezoresistive pressure sensors have diverse applications, spanning electronic skin, robotic limbs, and cardiovascular monitoring. Recent advancements in wearable health monitoring devices utilizing these sensors show promise for continuous cardiovascular monitoring. However, a substantial gap exists between laboratory development and market availability for pressure sensor based pulse monitoring. Challenges include the lack of effective testing schemes ensuring robust wearable operation and the necessity of bulky signal acquisition equipment, limiting adaptability. This study addresses these challenges by integrating the outputs of multiple flexible, skin-mountable piezoresistive sensors to create a robust wearable platform. This platform encompasses both sensing and miniaturized signal acquisition for arterial pulse waveform monitoring. The single piezoresistive sensor in our design exhibits sensitivity (1.4 kPa-1 at 2 – 8 kPa). This robust signal allows for the use of simple, compact signal collection devices mounted on a wristband. Moreover, the capability of the the system allows for detection of the three distinct peaks associated with the full pulse waveform, including the systolic, diastolic, and reflected diastolic peaks. In validation, a comparison between the wrist pulse waveform obtained with the compact signal collection device and a standard sourcemeter (Keithley 2460) confirms the capability of our wearable system to resolve the three distinct peaks. This work contributes to bridging the gap between research and practical applications, offering a potential solution for widespread adoption of continuous cardiovascular monitoring technology in diverse settings.

Confinement and heating promoted RTP of flumequine, oxolinic acid and levofloxacin on papers for their detection and discrimination

Developing simple assays to identify and discriminate quinolones are highly desired for food safety, which remain a great challenge due to the interferences from food matrices and the minor variation on the molecular structure of massive quinolones. We proposed a room-temperature phosphorescence (RTP) assay for the quantitative detection and discrimination of quinolones, aided by a confinement and heating strategy. Quinolones were loaded into the porous framework of paper, which provided confinement effects on the molecular motions of quinolones. The heating process further removed the solvents, eliminating their quenching effects on excitons. These synergistic effects improved the RTP intensity and emission lifetime by 1144-fold and from nanoseconds to seconds, respectively. Three types of quinolones were quantitatively detected and discriminated through pattern recognition methods. The proposed assay showed excellent detection performance in complicated meat samples, aided by the delayed signal collecting of RTP. The reported results provided a clue to modulate the RTP properties of organic molecules, showing great potential for application prospect in food safety analysis, environmental and healthy monitoring.