The Internet of Things (IoT) provides everyday objects and environments with “intelligence” and data connectivity to improve quality of life and the efficiency of a wide range of human activities. ...However, the ongoing exponential growth of the IoT device ecosystem—up to tens of billions of units to date—poses a challenge regarding how to power such devices. This Progress Report discusses how energy harvesting can address this challenge. It then discusses how indoor photovoltaics (IPV) constitutes an attractive energy harvesting solution, given its deployability, reliability, and power density. For IPV to provide an eco‐friendly route to powering IoT devices, it is crucial that its underlying materials and fabrication processes are low‐toxicity and not harmful to the environment over the product life cycle. A range of IPV technologies—both incumbent and emerging—developed to date is discussed, with an emphasis on their environmental sustainability. Finally, IPV based on emerging lead‐free perovskite‐inspired absorbers are examined, highlighting their status and prospects for low‐cost, durable, and efficient energy harvesting that is not harmful to the end user and environment. By examining emerging avenues for eco‐friendly IPV, timely insight is provided into promising directions toward IPV that can sustainably power the IoT revolution.
The rapid growth of the Internet of Things (IoT) ecosystem is bringing about a more interconnected society, but it also entails an exponential growth in power consumption from autonomous devices. Indoor photovoltaics (IPV) are a highly promising route to sustainably address this. However, their large‐scale deployment demands a critical evaluation of the environmental impact of the underlying materials and manufacturing processes.
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.
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•2D materials cannot enhance intensity-modulated SPR sensors under the investigated conditions.•The sensitivity of angular-modulated SPR sensor depends on the RI and thickness of 2D materials.•2D material’s thickness needed for the maximum sensitivity decreases with the increase of RI.•5 nm-thickness MoS2 enhanced SPR biosensor exhibited excellent performance for detection of SAs.
With the exponential rise in the market value and number of devices part of the Internet of Things (IoT), the demand for indoor photovoltaics (IPV) to power autonomous devices is predicted to rapidly ...increase. Lead‐free perovskite‐inspired materials (PIMs) have recently attracted significant attention in photovoltaics research, due to the similarity of their electronic structure to high‐performance lead‐halide perovskites, but without the same toxicity limitations. However, the capability of PIMs for indoor light harvesting has not yet been considered. Herein, two exemplar PIMs, BiOI and Cs3Sb2ClxI9‐x are examined. It is shown that while their bandgaps are too wide for single‐junction solar cells, they are close to the optimum for indoor light harvesting. As a result, while BiOI and Cs3Sb2ClxI9‐x devices are only circa 1%‐efficient under 1‐sun illumination, their efficiencies increase to 4–5% under indoor illumination. These efficiencies are within the range of reported values for hydrogenated amorphous silicon, i.e., the industry standard for IPV. It is demonstrated that such performance levels are already sufficient for millimeter‐scale PIM devices to power thin‐film‐transistor circuits. Intensity‐dependent and optical loss analyses show that future improvements in efficiency are possible. Furthermore, calculations of the optically limited efficiency of these and other low‐toxicity PIMs reveal their considerable potential for IPV, thus encouraging future efforts for their exploration for powering IoT devices.
Lead‐free perovskite‐inspired materials (PIMs) provide a particularly attractive route to low‐toxicity indoor photovoltaics (IPV). Two exemplar PIMs, bismuth oxyiodide (BiOI) and Cs3Sb2ClxI9‐x, deliver an IPV efficiency of 4–5%, and can power thin‐film‐transistor electronics. Loss analyses and calculations of the optically limited efficiency reveal that further efficiency increases are possible, encouraging future efforts for the exploration of PIMs for powering Internet of Things (IoT) devices.
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.
Point-of-care testing (POCT) can play a critical role in healthcare systems and societies, especially during situations like the COVID-19 outbreak.The successful commercialisation of such devices requires vital factors to be taken into consideration during development, including high sensitivity for specific analytes, fast and affordable results, minimum operational training, and multiplexing.Over the recent years, graphene has been extensively studied due to its advanced electrical properties and biocompatibility, with the potentials to transform the POCT market.Graphene-based biosensors have been developed in a broad range of applications, including the detection of nucleic acids, proteins, and cells with superior performance.Such sensors have the potential to be compared with existing methods currently applied in central laboratories and become available near the patient.
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.
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.
Abstract
Materials adopted in electronic gas sensors, such as chemiresistive-based NO
2
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 NO
2
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/MoS
2
) and carrying out in situ reduction of the GO to Reduced Graphene Oxide (RGO). The GO/MoS
2
e-textile was selective to NO
2
and showed sensitivity to 20 ppb NO
2
in dry air (0.05%/ppb) and 100 ppb NO
2
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 cm
2
)—on-body personal exposure monitoring.
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.
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
at 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
under 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.
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.
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