Derivative Method to Detect Sleep and Awake States through Heart Rate Variability Analysis Using Machine Learning Algorithms.

Sleep disorders can have harmful consequences in both the short and long term. They can lead to attention deficits, as well as cardiac, neurological and behavioral repercussions. One of the most widely used methods for assessing sleep disorders is polysomnography (PSG). A major challenge associated with this method is all the cables needed to connect the recording devices, making the examination more intrusive and usually requiring a clinical environment. This can have potential consequences on the test results and their accuracy. One simple way to assess the state of the central nervous system (CNS), a well-known indicator of sleep disorder, could be the use of a portable medical device. With this in mind, we implemented a simple model using both the RR interval (RRI) and its second derivative to accurately predict the awake and napping states of a subject using a feature classification model. For training and validation, we used a database providing measurements from nine healthy young adults (six men and three women), in which heart rate variability (HRV) associated with light-on, light-off, sleep onset and sleep offset events. Results show that using a 30 min RRI time series window suffices for this lightweight model to accurately predict whether the patient was awake or napping.

All Screen Printed and Flexible Silicon Carbide NTC Thermistors for Temperature Sensing Applications.

In this study, Silicon Carbide (SiC) nanoparticle-based serigraphic printing inks were formulated to fabricate highly sensitive and wide temperature range printed thermistors. Inter-digitated electrodes (IDEs) were screen printed onto Kapton® substrate using commercially avaiable silver ink. Thermistor inks with different weight ratios of SiC nanoparticles were printed atop the IDE structures to form fully printed thermistors. The thermistors were tested over a wide temperature range form 25 °C to 170 °C, exhibiting excellent repeatability and stability over 15 h of continuous operation. Optimal device performance was achieved with 30 wt.% SiC-polyimide ink. We report highly sensitive devices with a TCR of -0.556%/°C, a thermal coefficient of 502 K (ß-index) and an activation energy of 0.08 eV. Further, the thermistor demonstrates an accuracy of ±1.35 °C, which is well within the range offered by commercially available high sensitivity thermistors. SiC thermistors exhibit a small 6.5% drift due to changes in relative humidity between 10 and 90%RH and a 4.2% drift in baseline resistance after 100 cycles of aggressive bend testing at a 40° angle. The use of commercially available low-cost materials, simplicity of design and fabrication techniques coupled with the chemical inertness of the Kapton® substrate and SiC nanoparticles paves the way to use all-printed SiC thermistors towards a wide range of applications where temperature monitoring is vital for optimal system performance.

Recent Advances in the Photonic Curing of the Hole Transport Layer, the Electron Transport Layer, and the Perovskite Layers to Improve the Performance of Perovskite Solar Cells.

Perovskite solar cells (PSCs) have attracted increasing research interest, but their performance depends on both the choice of materials and the process used. The materials can typically be treated in solution, which makes them well suited for roll-to-roll processing methods, but their deposition under ambient conditions requires overcoming some challenges to improve stability and efficiency. In this review, we highlight the latest advancements in photonic curing (PC) for perovskite materials, as well as for hole transport layer (HTL) and electron transport layer (ETL) materials. We present how PC parameters can be used to control the optical, electrical, morphological, and structural properties of perovskite HTL and ETL layers. Emphasizing the significance of these advancements for perovskite solar cells could further highlight the importance of this research and underline its essential role in creating more efficient and sustainable solar technology.

Heart Rate Measurement Using the Built-In Triaxial Accelerometer from a Commercial Digital Writing Device.

Currently, wearable technology is an emerging trend that offers remarkable access to our data through smart devices like smartphones, watches, fitness trackers and textiles. As such, wearable devices can enable health monitoring without disrupting our daily routines. In clinical settings, electrocardiograms (ECGs) and photoplethysmographies (PPGs) are used to monitor heart and respiratory behaviors. In more practical settings, accelerometers can be used to estimate the heart rate when they are attached to the chest. They can also help filter out some noise in ECG signals from movement. In this work, we compare the heart rate data extracted from the built-in accelerometer of a commercial smart pen equipped with sensors (STABILO's DigiPen) to standard ECG monitor readouts. We demonstrate that it is possible to accurately predict the heart rate from the smart pencil. The data collection is carried out with eight volunteers writing the alphabet continuously for five minutes. The signal is processed with a Butterworth filter to cut off noise. We achieve a mean-squared error (MSE) better than 6.685 × 10-3 comparing the DigiPen's computed Δt (time between pulses) with the reference ECG data. The peaks' timestamps for both signals all maintain a correlation higher than 0.99. All computed heart rates (HR =60Δt) from the pen accurately correlate with the reference ECG signals.

Photonic post-processing of a multi-material transparent conductive electrode architecture for optoelectronic device integration.

Emerging flexible optoelectronic devices require multi-material processing capabilities to fully enable the use of temperature-sensitive substrates and materials. This report demonstrates how photonic sintering enables the processing of materials with very different properties. For example, charge carrier transport/blocking metal-oxides, and transparent conductive silver nanowire-based electrodes ought to be compatible with low-energy and high-throughput processing for integration onto flexible low-temperature substrates. Compared to traditional post-processing methods, we show a rapid fabrication route yielding highly-stable hybrid electrode architectures on polyethylene terephthalate (PET). This architecture consists of an interconnected silver nanowire network encapsulated with a thin crystalline photo-sensitive titanium dioxide (TiO2) coating, allowing both layers to be treated using independent photonic post-processing sintering steps. The first step sinters the nanowires, while the second completes the conversion of the top metal-oxide layer from amorphous to crystalline TiO2. This approach improves on the fabrication speed compared to oven processing, while delivering optical and electrical characteristics comparable to the state of the art. Optimized transparency values reach 85% with haze values down-to 7% at 550 nm, while maintaining a sheet resistance of 18.1 Ω sq.-1. However, this hybrid architecture provides a much stronger resilience to degradation, which we demonstrate through exposure to harsh plasma conditions. In summary, this study shows how carefully-optimized photonic curing post-processing can provide more-stable hybrid architectures while using a multi-material processing technique suitable for high-volume manufacturing on low-temperature substrates.

Editorial: Focus on green nanomaterials for a sustainable internet of things.

In the dynamic landscape of the Internet of Things (IoT), where smart devices are reshaping our world, nanomaterials can play a pivotal role in ensuring the IoT's sustainability. These materials are poised to redefine the development of smart devices, not only enabling cost-effective fabrication but also unlocking novel functionalities. As the IoT is set to encompass an astounding number of interconnected devices, the demand for environmentally friendly nanomaterials takes center stage. ThisFocus Issuespotlights cutting-edge research that explores the intersection of nanomaterials and sustainability. The collection delves deep into this critical nexus, encompassing a wide range of topics, from fundamental properties to applications in devices (e.g. sensors, optoelectronic synapses, energy harvesters, memory components, energy storage devices, and batteries), aspects concerning circularity and green synthesis, and an array of materials comprising organic semiconductors, perovskites, quantum dots, nanocellulose, graphene, and two-dimensional semiconductors. Authors not only showcase advancements but also delve into the sustainability profile of these materials, fostering a responsible endeavour toward a green IoT future.

Deep learning framework for sensor array precision and accuracy enhancement.

In the upcoming years, artificial intelligence is going to transform the practice of medicine in most of its specialties. Deep learning can help achieve better and earlier problem detection, while reducing errors on diagnosis. By feeding a deep neural network (DNN) with the data from a low-cost and low-accuracy sensor array, we demonstrate that it becomes possible to significantly improve the measurements' precision and accuracy. The data collection is done with an array composed of 32 temperature sensors, including 16 analog and 16 digital sensors. All sensors have accuracies between [Formula: see text]. 800 vectors are extracted, covering a range from to 30 to [Formula: see text]. In order to improve the temperature readings, we use machine learning to perform a linear regression analysis through a DNN. In an attempt to minimize the model's complexity in order to eventually run inferences locally, the network with the best results involves only three layers using the hyperbolic tangent activation function and the Adam Stochastic Gradient Descent optimizer. The model is trained with a randomly-selected dataset using 640 vectors (80% of the data) and tested with 160 vectors (20%). Using the mean squared error as a loss function between the data and the model's prediction, we achieve a loss of only 1.47x10[Formula: see text] on the training set and 1.22x10[Formula: see text] on the test set. As such, we believe this appealing approach offers a new pathway towards significantly better datasets using readily-available ultra low-cost sensors.

Continuous Critical Respiratory Parameter Measurements Using a Single Low-Cost Relative Humidity Sensor: Evaluation Study.

BACKGROUND: Accurate and portable respiratory parameter measurements are critical for properly managing chronic obstructive pulmonary diseases (COPDs) such as asthma or sleep apnea, as well as controlling ventilation for patients in intensive care units, during surgical procedures, or when using a positive airway pressure device for sleep apnea. OBJECTIVE: The purpose of this research is to develop a new nonprescription portable measurement device that utilizes relative humidity sensors (RHS) to accurately measure key respiratory parameters at a cost that is approximately 10 times less than the industry standard. METHODS: We present the development, implementation, and assessment of a wearable respiratory measurement device using the commercial Bosch BME280 RHS. In the initial stage, the RHS was connected to the pneumotach (PNT) gold standard device via its external connector to gather breathing metrics. Data collection was facilitated using the Arduino platform with a Bluetooth Low Energy connection, and all measurements were taken in real time without any additional data processing. The device's efficacy was tested with 7 participants (5 men and 2 women), all in good health. In the subsequent phase, we specifically focused on comparing breathing cycle and respiratory rate measurements and determining the tidal volume by calculating the region between inhalation and exhalation peaks. Each participant's data were recorded over a span of 15 minutes. After the experiment, detailed statistical analysis was conducted using ANOVA and Bland-Altman to examine the accuracy and efficiency of our wearable device compared with the traditional methods. RESULTS: The perfused air measured with the respiratory monitor enables clinicians to evaluate the absolute value of the tidal volume during ventilation of a patient. In contrast, directly connecting our RHS device to the surgical mask facilitates continuous lung volume monitoring. The results of the 1-way ANOVA showed high P values of .68 for respiratory volume and .89 for respiratory rate, which indicate that the group averages with the PNT standard are equivalent to those with our RHS platform, within the error margins of a typical instrument. Furthermore, analysis utilizing the Bland-Altman statistical method revealed a small bias of 0.03 with limits of agreement (LoAs) of -0.25 and 0.33. The RR bias was 0.018, and the LoAs were -1.89 and 1.89. CONCLUSIONS: Based on the encouraging results, we conclude that our proposed design can be a viable, low-cost wearable medical device for pulmonary parametric measurement to prevent and predict the progression of pulmonary diseases. We believe that this will encourage the research community to investigate the application of RHS for monitoring the pulmonary health of individuals.

Research Progress on Graphite-Derived Materials for Electrocatalysis in Energy Conversion and Storage.

High-performance electrocatalysts are critical to support emerging electrochemical energy storage and conversion technologies. Graphite-derived materials, including fullerenes, carbon nanotubes, and graphene, have been recognized as promising electrocatalysts and electrocatalyst supports for the oxygen reduction reaction (ORR), oxygen evolution reaction (OER), hydrogen evolution reaction (HER), and carbon dioxide reduction reaction (CO2RR). Effective modification/functionalization of graphite-derived materials can promote higher electrocatalytic activity, stability, and durability. In this review, the mechanisms and evaluation parameters for the above-outlined electrochemical reactions are introduced first. Then, we emphasize the preparation methods for graphite-derived materials and modification strategies. We further highlight the importance of the structural changes of modified graphite-derived materials on electrocatalytic activity and stability. Finally, future directions and perspectives towards new and better graphite-derived materials are presented.

Deep-learning framework for fully-automated recognition of TiO2 polymorphs based on Raman spectroscopy.

Emerging machine learning techniques can be applied to Raman spectroscopy measurements for the identification of minerals. In this project, we describe a deep learning-based solution for automatic identification of complex polymorph structures from their Raman signatures. We propose a new framework using Convolutional Neural Networks and Long Short-Term Memory networks for compound identification. We train and evaluate our model using the publicly-available RRUFF spectral database. For model validation purposes, we synthesized and identified different TiO2 polymorphs to evaluate the performance and accuracy of the proposed framework. TiO2 is a ubiquitous material playing a crucial role in many industrial applications. Its unique properties are currently used advantageously in several research and industrial fields including energy storage, surface modifications, optical elements, electrical insulation to microelectronic devices such as logic gates and memristors. The results show that our model correctly identifies pure Anatase and Rutile with a high degree of confidence. Moreover, it can also identify defect-rich Anatase and modified Rutile based on their modified Raman Spectra. The model can also correctly identify the key component, Anatase, from the P25 Degussa TiO2. Based on the initial results, we firmly believe that implementing this model for automatically detecting complex polymorph structures will significantly increase the throughput, while dramatically reducing costs.

Synthesis of vacancy-rich titania particles suitable for the additive manufacturing of ceramics.

In the last decades, titania (or TiO2) particles played a crucial role in the development of photo-catalysis and better environmentally-friendly energy-harvesting techniques. In this work, we engineer a new generation of TiO2 particles rich in oxygen vacancies using a modified sol-gel synthesis. By design, these vacancy-rich particles efficiently absorb visible light to allow carefully-controlled light-induced conversion to the anatase or rutile crystalline phases. FTIR and micro-Raman spectroscopy reveal the formation of oxygen vacancies during conversion and explain this unique laser-assisted crystallization mechanism. We achieve low-energy laser-assisted crystallization in ambient environment using a modified filament 3D printer equipped with a low-power laser printhead. Since the established high-temperature treatment necessary to convert to crystalline TiO2 is ill-suited to additive manufacturing platforms, this work removes a major fundamental hurdle and opens whole new vistas of possibilities towards the additive manufacturing of ceramics, including carefully-engineered crystalline TiO2 substrates with potential applications for new and better photo-catalysis, fuel cells and energy-harvesting technologies.

Screen-printed p-n BiOCl/BiFeO3 heterojunctions for efficient photocatalytic degradation of Rhodamine B.

Colloidal-free screen-printed p-n BiOCl/BiFeO3 heterojunctions are successfully synthesized to achieve photocatalytic degradation of Rhodamine B (RhB) using visible light (λ ≥ 400 nm). The crystalline structure of dense BiOCl nanosheets self-assembled with impressive aspect ratio atop BFO powders is confirmed by XRD, Raman and TEM measurements. Iron impurities inside these 10 ± 2 nm-thick BiOCl nanosheets increase visible light absorption. Fluorescent Rhodamine B (RhB) dye degradation is used to evaluate the photocatalytic performance of this unique heterojunction material. For optimal metal-enhanced RhB degradation, a few nanometers of platinum are deposited using the sputtering technique to act as a cocatalyst. This unique architecture yields an impressive 92% RhB degradation in only 150 min under visible light. Operating at near-neutral pH, the proposed approach also addresses the key issue of catalysis recovery, which remains one of the main drawbacks of current photocatalysis technologies.

End-to-end deep learning framework for printed circuit board manufacturing defect classification.

We report a complete deep-learning framework using a single-step object detection model in order to quickly and accurately detect and classify the types of manufacturing defects present on Printed Circuit Board (PCBs). We describe the complete model architecture and compare with the current state-of-the-art using the same PCB defect dataset. These benchmark methods include the Faster Region Based Convolutional Neural Network (FRCNN) with ResNet50, RetinaNet, and You-Only-Look-Once (YOLO) for defect detection and identification. Results show that our method achieves a 98.1% mean average precision(mAP[IoU = 0.5]) on the test samples using low-resolution images. This is 3.2% better than the state-of-the-art using low-resolution images (YOLO V5m) and 1.4% better than the state-of-the-art using high-resolution images (FRCNN-ResNet FPN). While achieving better accuracies, our model also requires roughly 3× fewer model parameters (7.02M) compared with the state-of-the-art FRCNN-ResNet FPN (23.59M) and YOLO V5m (20.08M). In most cases, the major bottleneck of the PCB manufacturing chain is quality control, reliability testing and manual rework of defective PCBs. Based on the initial results, we firmly believe that implementing this model on a PCB manufacturing line could significantly increase the production yield and throughput, while dramatically reducing manufacturing costs.

High-performance silver nanowires transparent conductive electrodes fabricated using manufacturing-ready high-speed photonic sinterization solutions.

On the long road towards low-cost flexible hybrid electronics, integration and printable solar energy harvesting solutions, there is an urgent need for high-performance transparent conductive electrodes produced using manufacturing-ready techniques and equipment. In recent years, randomly-distributed metallic nanowire-based transparent mesh electrodes have proven highly-promising as they offer a superb compromise between high performances and low fabrication costs. Unfortunately, these high figure-of-merit transparent mesh electrodes usually rely heavily on extensive post-deposition processing. While conventional thermal annealing yields good performances, it is especially ill-suited for deposition on low-temperature substrates or for high-throughput manufacturing solutions. Similarly, laser-induced annealing severely limits the processing time for electrodes covering large surfaces. In this paper, we report the fabrication of ultra high-performance silver nanowires-based transparent conductive electrodes fabricated using optimized manufacturing-ready ultrafast photonic curing solutions. Using conventional indium tin oxide (ITO) as our benchmark for transparent electrodes, we demonstrate a 2.6-2.7 [Formula: see text] performance gain using two different figure-of-merit indicators. Based on these results, we believe this research provides an ideal manufacturing-ready approach for the large-scale and low-cost fabrication of ultra high-performance transparent electrodes for flexible hybrid electronics and solar-energy harvesting applications.

One-step photonic curing of screen-printed conductive Ni flake electrodes for use in flexible electronics.

Photonic curing has shown great promise in maintaining the integrity of flexible thin polymer substrates without structural degradation due to shrinkage, charring or decomposition during the sintering of printed functional ink films in milliseconds at high temperatures. In this paper, single-step photonic curing of screen-printed nickel (Ni) electrodes is reported for sensor, interconnector and printed electronics applications. Solid bleached sulphate paperboard (SBS) and polyethylene terephthalate polymer (PET) substrates are employed to investigate the electrical performance, ink transfer and ink spreading that directly affect the fabrication of homogeneous ink films. Ni flake ink is selected, particularly since its effects on sintering and rheology have not yet been examined. The viscosity of Ni flake ink yields shear-thinning behavior that is distinct from that of screen printing. The porous SBS substrate is allowed approximately 20% less ink usage. With one-step photonic curing, the electrodes on SBS and PET exhibited electrical performances of a minimum of 4 Ω/sq and 16 Ω/sq, respectively, at a pulse length of 1.6 ms, which is comparable to conventional thermal heating at 130 °C for 5 min. The results emphasize the suitability of Ni flake ink to fabricate electronic devices on flexible substrates by photonic curing.

High performance BiFeO3 ferroelectric nanostructured photocathodes.

Ferroelectric materials may be used as effective photoelectrocatalysts for water splitting due to enhanced charge carrier separation driven by their spontaneous polarization induced internal electric field. Compared to other ferroelectric materials, BiFeO3 exhibits a high catalytic efficiency due to its comparatively smaller bandgap, which enables light absorption from a large part of the solar spectrum and its higher bulk ferroelectric polarization. Here, we compare the photoelectrochemical properties of three different BiFeO3 morphologies, namely, nanofibers, nanowebs, and thin films synthesized via electrospinning, directly on fluorine-doped tin oxide (FTO) coated glass substrates. A significant photocathodic current in the range from -86.2 to -56.5 µA cm-2 at -0.4 V bias (vs Ag/AgCl) has been recorded for all three morphologies in 0.1M Na2SO4 aqueous solution (pH = 6.8). Among these morphologies, BiFeO3 nanofibers exhibit higher efficiency because of their larger surface area and improved charge separation resulting from rapid diffusion of photoinduced charge carriers along the axis of the nanofiber. In the case of BiFeO3 nanofibers, we obtained the highest photocurrent density of -86.2 µA/cm2 at -0.4 V bias (vs Ag/AgCl electrode) and an onset potential of 0.22 V. We also observed that the onset potential of the photocathodic current can be increased by applying a positive polarization voltage, which leads to favorable bending of band edges at the electrode/electrolyte interface resulting in increased charge carrier separation.

Low-cost photodetector architectures fabricated at room-temperature using nano-engineered silicon wafer and sol-gel TiO2 - based heterostructures.

In the last decades, significant research has been done on the nanocrystalline forms of titanium dioxide (TiO2). Amorphous TiO2 has not been studied intensively despite being significantly less expensive compared to crystalline TiO2. This study reveals significant improvement in UV-VIS photodetection properties from heterostructures fabricated in ambient environment using n-type silicon nanowire arrays and amorphous TiO2 sol-gel. Our ultra-low-cost UV-VIS photodetectors can cover a wide range of applications. We report fast rise/decay time constants of 0.23 ms/0.17 ms and high responsivity up-to 6.0 A/W in the UV and 25.0 A/W in the visible range under low (1 V) external bias. The large surface area due to the nanowire array architecture leads to 2 orders of magnitude enhancement in photo-response. Besides the final electrode deposition, the entire device fabrication is performed using low-cost, all solution-based methods in ambient conditions. These low-cost UV-Visible broadband photodetectors can potentially serve a wide range of applications.

Hybrid Color-Tunable Polymer Light-Emitting Diodes Using Electrospraying.

This paper proposes a new paradigm in polymer light-emitting diode (PLED) fabrication by using a uniform electrosprayed microparticle film as the active layer. Among the seven electrospraying parameters analyzed, three crucial parameters are statistically identified and optimized to obtain thin electrosprayed microparticle layers. Using optimized electrospraying conditions, single-color red-emitting PLED (MEH-PPV) with a peak current density of 16.1 mA/mm2 under a 13.5 V bias and a peak external quantum efficiency of 3.2% are successfully fabricated. Finally, a combinatorial approach is implemented using both MEH-PPV (red-emitting) and F8BT (green-emitting) polymer microparticles at different mixing ratios to tune the emission spectrum of the devices. As such, it has been demonstrated that hybrid multilayer films using different organic materials with nonorthogonal solvents can be produced using this new approach. The parameter analysis and color-tunable properties pave the way towards white light PLED fabrication.

Halide Perovskite Nanocrystals for Next-Generation Optoelectronics.

Colloidal perovskite nanocrystals (PNCs) combine the outstanding optoelectronic properties of bulk perovskites with strong quantum confinement effects at the nanoscale. Their facile and low-cost synthesis, together with superior photoluminescence quantum yields and exceptional optical versatility, make PNCs promising candidates for next-generation optoelectronics. However, this field is still in its early infancy and not yet ready for commercialization due to several open challenges to be addressed, such as toxicity and stability. Here, the key synthesis strategies and the tunable optical properties of PNCs are discussed. The photophysical underpinnings of PNCs, in correlation with recent developments of PNC-based optoelectronic devices, are especially highlighted. The final goal is to outline a theoretical scaffold for the design of high-performance devices that can at the same time address the commercialization challenges of PNC-based technology.

Highly Efficient and Ultrasensitive Large-Area Flexible Photodetector Based on Perovskite Nanowires.

Hybrid organic-inorganic perovskites have shown exceptional semiconducting properties and microstructural versatility for inexpensive, solution-processable photovoltaic and optoelectronic devices. In this work, an all-solution-based technique in ambient environment for highly sensitive and high-speed flexible photodetectors using high crystal quality perovskite nanowires grown on Kapton substrate is presented. At 10 V, the optimized photodetector exhibits a responsivity as high as 0.62 A W-1 , a maximum specific detectivity of 7.3 × 1012 cm Hz1/2 W-1 , and a rise time of 227.2 µs. It also shows remarkable photocurrent stability even beyond 5000 bending cycles. Moreover, a deposition of poly(methyl methacrylate) (PMMA) as a protective layer on the perovskite yields significantly better stability under ambient air operation: the PMMA-protected devices are stable for over 30 days. This work demonstrates a cost-effective fabrication technique for high-performance flexible photodetectors and opens opportunities for research advancements in broadband and large-scale flexible perovskite-based optoelectronic devices.