Spray-lithography of hybrid graphene-perovskite paper-based photodetectors for sustainable electronics.

Paper is an ideal substrate for the development of flexible and environmentally sustainable ubiquitous electronic systems. When combined with nanomaterial-based devices, it can be harnessed for various Internet-of-Things applications, ranging from wearable electronics to smart packaging. However, paper remains a challenging substrate for electronics due to its rough and porous nature. In addition, the absence of established fabrication methods is impeding its utilization in wearable applications. Unlike other paper-based electronics with added layers, in this study, we present a scalable spray-lithography on a commercial paper substrate. We present a non-vacuum spray-lithography of chemical vapor deposition (CVD) single-layer graphene (SLG), carbon nanotubes (CNTs) and perovskite quantum dots (QDs) on a paper substrate. This approach combines the advantages of two large-area techniques: CVD and spray-coating. The first technique allows for the growth of SLG, while the second enables the spray coating of a mask to pattern CVD SLG, electrodes (CNTs), and photoactive (QDs) layers. We harness the advantages of perovskite QDs in photodetection, leveraging their strong absorption coefficients. Integrating them with the graphene enhances the photoconductive gain mechanism, leading to high external responsivity. The presented device shows high external responsivity of ∼520 A W-1at 405 nm at <1 V bias due to the photoconductive gain mechanism. The prepared paper-based photodetectors (PDs) achieve an external responsivity of 520 A W-1under 405 nm illumination at <1 V operating voltage. To the best of our knowledge, our devices have the highest external responsivity among paper-based PDs. By fabricating arrays of PDs on a paper substrate in the air, this work highlights the potential of this scalable approach for enabling ubiquitous electronics on paper.

Finite Element Simulation Model of Metallic Thermal Conductivity Detectors for Compact Air Pollution Monitoring Devices.

Air pollution has been associated with several health problems. Detecting and measuring the concentration of harmful pollutants present in complex air mixtures has been a long-standing challenge, due to the intrinsic difficulty of distinguishing among these substances from interferent species and environmental conditions, both indoor and outdoor. Despite all efforts devoted by the scientific and industrial communities to tackling this challenge, the availability of suitable device technologies able to selectively discriminate these pollutants present in the air at minute, yet dangerous, concentrations and provide a quantitative measure of their concentrations is still an unmet need. Thermal conductivity detectors (TCDs) show promising characteristics that make them ideal gas sensing tools capable of recognising different gas analytes based on their physical fingerprint characteristics at the molecular level, such as their density, thermal conductivity, dynamic viscosity, and others. In this paper, the operation of TCD gas sensors is presented and explored using a finite element simulation of Joule heating in a sensing electrode placed in a gas volume. The results obtained show that the temperature, and hence, the resistance of the individual suspended microbridge sensor device, depends on the surrounding gas and its thermal conductivity, while the sensitivity and power consumption depend on the properties of the constitutive metal. Moreover, the electrode resistance is proven to be linearly dependent on the applied voltage.

Pathogen Detection via Impedance Spectroscopy-Based Biosensor.

This paper presents the development of a miniaturized sensor device for selective detection of pathogens, specifically Influenza A Influenza virus, as an enveloped virus is relatively vulnerable to damaging environmental impacts. In consideration of environmental factors such as humidity and temperature, this particular pathogen proves to be an ideal choice for our study. It falls into the category of pathogens that pose greater challenges due to their susceptibility. An impedance biosensor was integrated into an existing platform and effectively separated and detected high concentrations of airborne pathogens. Bio-functionalized hydrogel-based detectors were utilized to analyze virus-containing particles. The sensor device demonstrated high sensitivity and specificity when exposed to varying concentrations of Influenza A virus ranging from 0.5 to 50 µg/mL. The sensitivity of the device for a 0.5 µg/mL analyte concentration was measured to be 695 Ω· mL/µg. Integration of this pathogen detector into a compact-design air quality monitoring device could foster the advancement of personal exposure monitoring applications. The proposed sensor device offers a promising approach for real-time pathogen detection in complex environmental settings.

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.

Machine Learning Methods for Automatic Silent Speech Recognition Using a Wearable Graphene Strain Gauge Sensor.

Silent speech recognition is the ability to recognise intended speech without audio information. Useful applications can be found in situations where sound waves are not produced or cannot be heard. Examples include speakers with physical voice impairments or environments in which audio transference is not reliable or secure. Developing a device which can detect non-auditory signals and map them to intended phonation could be used to develop a device to assist in such situations. In this work, we propose a graphene-based strain gauge sensor which can be worn on the throat and detect small muscle movements and vibrations. Machine learning algorithms then decode the non-audio signals and create a prediction on intended speech. The proposed strain gauge sensor is highly wearable, utilising graphene's unique and beneficial properties including strength, flexibility and high conductivity. A highly flexible and wearable sensor able to pick up small throat movements is fabricated by screen printing graphene onto lycra fabric. A framework for interpreting this information is proposed which explores the use of several machine learning techniques to predict intended words from the signals. A dataset of 15 unique words and four movements, each with 20 repetitions, was developed and used for the training of the machine learning algorithms. The results demonstrate the ability for such sensors to be able to predict spoken words. We produced a word accuracy rate of 55% on the word dataset and 85% on the movements dataset. This work demonstrates a proof-of-concept for the viability of combining a highly wearable graphene strain gauge and machine leaning methods to automate silent speech recognition.

Extracellular vesicles are independent metabolic units with asparaginase activity.

Extracellular vesicles (EVs) are membrane particles involved in the exchange of a broad range of bioactive molecules between cells and the microenvironment. Although it has been shown that cells can traffic metabolic enzymes via EVs, much remains to be elucidated with regard to their intrinsic metabolic activity. Accordingly, herein we assessed the ability of neural stem/progenitor cell (NSC)-derived EVs to consume and produce metabolites. Our metabolomics and functional analyses both revealed that EVs harbor L-asparaginase activity, catalyzed by the enzyme asparaginase-like protein 1 (Asrgl1). Critically, we show that Asrgl1 activity is selective for asparagine and is devoid of glutaminase activity. We found that mouse and human NSC EVs traffic Asrgl1. Our results demonstrate, for the first time, that NSC EVs function as independent metabolic units that are able to modify the concentrations of critical nutrients, with the potential to affect the physiology of their microenvironment.

Real-time, noise and drift resilient formaldehyde sensing at room temperature with aerogel filaments.

Formaldehyde, a known human carcinogen, is a common indoor air pollutant. However, its real-time and selective recognition from interfering gases remains challenging, especially for low-power sensors suffering from noise and baseline drift. We report a fully 3D-printed quantum dot/graphene-based aerogel sensor for highly sensitive and real-time recognition of formaldehyde at room temperature. By optimizing the morphology and doping of printed structures, we achieve a record-high and stable response of 15.23% for 1 part per million formaldehyde and an ultralow detection limit of 8.02 parts per billion consuming only ∼130-microwatt power. On the basis of measured dynamic response snapshots, we also develop intelligent computational algorithms for robust and accurate detection in real time despite simulated substantial noise and baseline drift, hitherto unachievable for room temperature sensors. Our framework in combining materials engineering, structural design, and computational algorithm to capture dynamic response offers unprecedented real-time identification capabilities of formaldehyde and other volatile organic compounds at room temperature.

Machine Learning-Assistant Colorimetric Sensor Arrays for Intelligent and Rapid Diagnosis of Urinary Tract Infection.

Urinary tract infections (UTIs), which can lead to pyelonephritis, urosepsis, and even death, are among the most prevalent infectious diseases worldwide, with a notable increase in treatment costs due to the emergence of drug-resistant pathogens. Current diagnostic strategies for UTIs, such as urine culture and flow cytometry, require time-consuming protocols and expensive equipment. We present here a machine learning-assisted colorimetric sensor array based on recognition of ligand-functionalized Fe single-atom nanozymes (SANs) for the identification of microorganisms at the order, genus, and species levels. Colorimetric sensor arrays are built from the SAN Fe1-NC functionalized with four types of recognition ligands, generating unique microbial identification fingerprints. By integrating the colorimetric sensor arrays with a trained computational classification model, the platform can identify more than 10 microorganisms in UTI urine samples within 1 h. Diagnostic accuracy of up to 97% was achieved in 60 UTI clinical samples, holding great potential for translation into clinical practice applications.

WMNN: Wearables-Based Multi-Column Neural Network for Human Activity Recognition.

In recent years, human activity recognition (HAR) technologies in e-health have triggered broad interest. In literature, mainstream works focus on the body's spatial information (i.e. postures) which lacks the interpretation of key bioinformatics associated with movements, limiting the use in applications requiring comprehensively evaluating motion tasks' correctness. To address the issue, in this article, a Wearables-based Multi-column Neural Network (WMNN) for HAR based on multi-sensor fusion and deep learning is presented. Here, the Tai Chi Eight Methods were utilized as an example as in which both postures and muscle activity strengths are significant. The research work was validated by recruiting 14 subjects in total, and we experimentally show 96.9% and 92.5% accuracy for training and testing, for a total of 144 postures and corresponding muscle activities. The method is then provided with a human-machine interface (HMI), which returns users with motion suggestions (i.e. postures and muscle strength). The report demonstrates that the proposed HAR technique can enhance users' self-training efficiency, potentially promoting the development of the HAR area.

Two-dimensional material-enhanced surface plasmon resonance for antibiotic sensing.

Two-dimensional (2D) materials attract attention from the academic community due to their excellent properties, and their wide application in sensing is expected to revolutionize environmental monitoring, medical diagnostics, and food safety. In this work, we systematically evaluate the effects of 2D materials on the Au chip surface plasmon resonance (SPR) sensor. The results reveal that 2D materials cannot improve the sensitivity of intensity-modulated SPR sensors. However, there exists an optimal real part of RI of 3.5-4.0 and optimal thickness when choosing nanomaterials for sensitivity enhancement of SPR sensors in angular modulation. In addition, the smaller the imaginary part of the nanomaterial RI, the higher the sensitivity of the proposed Au SPR sensor. The 2D material's thickness needed for the highest sensitivity decreases with increasing real part and imaginary part of the RI. As a case study, we developed a 5 nm-thickness MoS2-enhanced SPR biosensor, which exhibited a low sulfonamides (SAs) detection limit of 0.05 µg/L based on a group-targeting indirect competitive immunoassay, nearly 12-fold lower than that of the bare Au SPR system. The proposed criteria help to shed light on the 2D material-Au surface interaction, which has greatly promoted the development of novel SPR biosensing with outstanding sensitivity.

Truly form-factor-free industrially scalable system integration for electronic textile architectures with multifunctional fiber devices.

An integrated textile electronic system is reported here, enabling a truly free form factor system via textile manufacturing integration of fiber-based electronic components. Intelligent and smart systems require freedom of form factor, unrestricted design, and unlimited scale. Initial attempts to develop conductive fibers and textile electronics failed to achieve reliable integration and performance required for industrial-scale manufacturing of technical textiles by standard weaving technologies. Here, we present a textile electronic system with functional one-dimensional devices, including fiber photodetectors (as an input device), fiber supercapacitors (as an energy storage device), fiber field-effect transistors (as an electronic driving device), and fiber quantum dot light-emitting diodes (as an output device). As a proof of concept applicable to smart homes, a textile electronic system composed of multiple functional fiber components is demonstrated, enabling luminance modulation and letter indication depending on sunlight intensity.

Unencapsulated and washable two-dimensional material electronic-textile for NO2 sensing in ambient air.

Materials adopted in electronic gas sensors, such as chemiresistive-based NO2 sensors, for integration in clothing fail to survive standard wash cycles due to the combined effect of aggressive chemicals in washing liquids and mechanical abrasion. Device failure can be mitigated by using encapsulation materials, which, however, reduces the sensor performance in terms of sensitivity, selectivity, and therefore utility. A highly sensitive NO2 electronic textile (e-textile) sensor was fabricated on Nylon fabric, which is resistant to standard washing cycles, by coating Graphene Oxide (GO), and GO/Molybdenum disulfide (GO/MoS2) and carrying out in situ reduction of the GO to Reduced Graphene Oxide (RGO). The GO/MoS2 e-textile was selective to NO2 and showed sensitivity to 20 ppb NO2 in dry air (0.05%/ppb) and 100 ppb NO2 in humid air (60% RH) with a limit of detection (LOD) of ~ 7.3 ppb. The selectivity and low LOD is achieved with the sensor operating at ambient temperatures (~ 20 °C). The sensor maintained its functionality after undergoing 100 cycles of standardised washing with no encapsulation. The relationship between temperature, humidity and sensor response was investigated. The e-textile sensor was embedded with a microcontroller system, enabling wireless transmission of the measurement data to a mobile phone. These results show the potential for integrating air quality sensors on washable clothing for high spatial resolution (< 25 cm2)-on-body personal exposure monitoring.

Coexistence of Contact Electrification and Dynamic p-n Junction Modulation Effects in Triboelectrification.

The triboelectric effect occurs when two dissimilar materials are in physical contact, attributed to the combination of contact electrification (CE) and electrostatic induction. It has been extensively explored for the development of high-performance triboelectric nanogenerators (TENGs). In this paper, we report on, besides the CE-related charge generation, an additional charge generation phenomenon associated with the modulation of the p-n junction when two semiconductor materials [methylammonium lead iodide (MAPI) and poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS)] are put in contact and separated dynamically. The electrical outputs generated by the CE effect are determined by the surface potential difference between the two friction materials, while the ones induced by the p-n junction modulation are determined by the dynamic variations in the depletion widths of the two semiconductor friction materials. The outputs generated by the CE effect and the p-n junction effect are well separated in time scale; the p-n junction modulation contributes ∼20% of the total charge generated and could be varied by changing the chemical composition of the semiconductors. The results may provide an alternative method for the development of high-performance TENGs by utilizing this additional p-n junction modulation effect.

Optoelectronic system and device integration for quantum-dot light-emitting diode white lighting with computational design framework.

We propose a computational design framework to design the architecture of a white lighting system having multiple pixelated patterns of electric-field-driven quantum dot light-emitting diodes. The quantum dot of the white lighting system has been optimised by a system-level combinatorial colour optimisation process with the Nelder-Mead algorithm used for machine learning. The layout of quantum dot patterns is designed precisely using rigorous device-level charge transport simulation with an electric-field dependent charge injection model. A theoretical maximum of 97% colour rendering index has been achieved with red, green, cyan, and blue quantum dot light-emitting diodes as primary colours. The white lighting system has been fabricated using the transfer printing technique to validate the computational design framework. It exhibits excellent lighting performance of 92% colour rendering index and wide colour temperature variation from 1612 K to 8903 K with only the four pixelated quantum dots as primary.

Smart textile lighting/display system with multifunctional fibre devices for large scale smart home and IoT applications.

Smart textiles consist of discrete devices fabricated from-or incorporated onto-fibres. Despite the tremendous progress in smart textiles for lighting/display applications, a large scale approach for a smart display system with integrated multifunctional devices in traditional textile platforms has yet to be demonstrated. Here we report the realisation of a fully operational 46-inch smart textile lighting/display system consisting of RGB fibrous LEDs coupled with multifunctional fibre devices that are capable of wireless power transmission, touch sensing, photodetection, environmental/biosignal monitoring, and energy storage. The smart textile display system exhibits full freedom of form factors, including flexibility, bendability, and rollability as a vivid RGB lighting/grey-level-controlled full colour display apparatus with embedded fibre devices that are configured to provide external stimuli detection. Our systematic design and integration strategies are transformational and provide the foundation for realising highly functional smart lighting/display textiles over large area for revolutionary applications on smart homes and internet of things (IoT).

Graphene for Biosensing Applications in Point-of-Care Testing.

Graphene and graphene-related materials (GRMs) exhibit a unique combination of electronic, optical, and electrochemical properties, which make them ideally suitable for ultrasensitive and selective point-of-care testing (POCT) devices. POCT device-based applications in diagnostics require test results to be readily accessible anywhere to produce results within a short analysis timeframe. This review article provides a summary of methods and latest developments in the field of graphene and GRM-based biosensing in POCT and an overview of the main applications of the latter in nucleic acids and enzymatic biosensing, cell detection, and immunosensing. For each application, we discuss scientific and technological advances along with the remaining challenges, outlining future directions for widespread use of this technology in biomedical applications.

Technology progress on quantum dot light-emitting diodes for next-generation displays.

Quantum dot light-emitting diodes (QD-LEDs) are widely recognised as great alternatives to organic light-emitting diodes (OLEDs) due to their enhanced performances. This focus article surveys the current progress on the state-of-the-art QD-LED technology including material synthesis, device optimization and innovative fabrication processes. A discussion on the material synthesis of core nanocrystals, shell layers and surface-binding ligands is presented for high photoluminescence quantum yield (PLQY) quantum dots (QDs) using heavy-metal free materials. The operational principles of several types of QD-LED device architectures are also covered, and the recent evolution of device engineering technologies is investigated. By exploring the fabrication process for pixel-patterning of QD-LEDs on an active-matrix backplane for full-colour display applications, we anticipate further improvement in device performance for the commercialisation of next-generation displays.

Double-Framed Thin Elastomer Devices.

Elastomers and, in particular, polydimethylsiloxane (PDMS) are widely adopted as biocompatible mechanically compliant substrates for soft and flexible micro-nanosystems in medicine, biology, and engineering. However, several applications require such low thicknesses (e.g., <100 µm) that make peeling-off critical because very thin elastomers become delicate and tend to exhibit strong adhesion with carriers. Moreover, microfabrication techniques such as photolithography use solvents which swell PDMS, introducing complexity and possible contamination, thus limiting industrial scalability and preventing many biomedical applications. Here, we combine low-adhesion and rectangular carrier substrates, adhesive Kapton frames, micromilling-defined shadow masks, and adhesive-neutralizing paper frames for enabling fast, easy, green, contaminant-free, and scalable manufacturing of thin elastomer devices, with both simplified peeling and handling. The accurate alignment between the frame and shadow masks can be further facilitated by micromilled marking lines on the back side of the low-adhesion carrier. As a proof of concept, we show epidermal sensors on a 50 µm-thick PDMS substrate for measuring strain, the skin bioimpedance and the heart rate. The proposed approach paves the way to a straightforward, green, and scalable fabrication of contaminant-free thin devices on elastomers for a wide variety of applications.

Ambipolar Deep-Subthreshold Printed-Carbon-Nanotube Transistors for Ultralow-Voltage and Ultralow-Power Electronics.

The development of ultralow-power and easy-to-fabricate electronics with potential for large-scale circuit integration (i.e., complementary or complementary-like) is an outstanding challenge for emerging off-the-grid applications, e.g., remote sensing, "place-and-forget", and the Internet of Things. Herein we address this challenge through the development of ambipolar transistors relying on solution-processed polymer-sorted semiconducting carbon nanotube networks (sc-SWCNTNs) operating in the deep-subthreshold regime. Application of self-assembled monolayers at the active channel interface enables the fine-tuning of sc-SWCNTN transistors toward well-balanced ambipolar deep-subthreshold characteristics. The significance of these features is assessed by exploring the applicability of such transistors to complementary-like integrated circuits, with respect to which the impact of the subthreshold slope and flatband voltage on voltage and power requirements is studied experimentally and theoretically. As demonstrated with inverter and NAND gates, the ambipolar deep-subthreshold sc-SWCNTN approach enables digital circuits with complementary-like operation and characteristics including wide noise margins and ultralow operational voltages (≤0.5 V), while exhibiting record-low power consumption (≤1 pW/µm). Among thin-film transistor technologies with minimal material complexity, our approach achieves the lowest energy and power dissipation figures reported to date, which are compatible with and highly attractive for emerging off-the-grid applications.

Robust In-Zn-O Thin-Film Transistors with a Bilayer Heterostructure Design and a Low-Temperature Fabrication Process Using Vacuum and Solution Deposited Layers.

We report on the design, fabrication, and characterization of heterostructure In-Zn-O (IZO) thin-film transistors (TFTs) with improved performance characteristics and robust operation. The heterostructure layer is fabricated by stacking a solution-processed IZO film on top of a buffer layer, which is deposited previously using an electron beam (e-beam) evaporator. A thin buffer layer at the dielectric interface can help to template the structure of the channel. The control of the precursors and of the solvent used during the sol-gel process can help lower the temperature needed for the sol-gel condensation reaction to proceed cleanly. This boosts the overall performance of the device with a significantly reduced subthreshold swing, a four-fold mobility increase, and a two-order of magnitude larger on/off ratio. Atomistic simulations of the a-IZO structure using molecular dynamics (both classical and ab initio) and hybrid density functional theory (DFT) calculations of the electronic structure reveal the potential atomic origin of these effects.