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.EP/S023046/

The potential of plasma-derived hard carbon for sodium-ion batteries

Sodium-ion batteries (SIB) are receiving wider attention due to sodium abundance and lower cost. The application of hard carbon to SIB electrodes has shown their significant potential to increase rates, capacities, stability, and overall performance. This article describes the significance of hard carbon, its structural models, and mechanisms for SIB applications. Further, this work unveils the potential of plasma methods as a scalable and sustainable manufacturing source of hard carbon to meet its increasing industrial demands for energy storage applications. The working mechanisms of major plasma technologies, the influence of their parameters on carbon structure, and their suitability for SIB applications are described. This work summarises the performance of emerging plasma-driven hard carbon solutions for SIB, including extreme environments, and revolves around the flexibilities offered by plasma methods in a wider spectrum such as multi-materials doping, in-situ multilayer fabrication, and a broad range of formulations and environments to deposit hard carbon-based electrodes for superior SIB performance. It is conceived the challenges around the stable interface, capacity fading, and uplifting SIB capacities and rates at higher voltage are currently being researched, Whereas, the development of real-time monitoring and robust diagnostic tools for SIB are new horizons. This work proposes a data-driven framework for plasma-driven hard carbon to make high-performance energy storage batteries

An electronic textile embedded smart cementitious composite

Abstract: Structural health monitoring (SHM) using self‐sensing cement‐based materials has been reported before, where nano‐fillers have been incorporated in cementitious matrices as functional sensing elements. A percolation threshold is always required in order for conductive nano‐fillers modified concrete to be useful for SHM. Nonetheless, the best pressure/strain sensitivity results achieved for any self‐sensing cementitious matrix are <0.01 MPa−1. In this work, we introduce for the first‐time novel partially reduced graphene oxide based electronic textile (e‐textile) embedded in plain and as well as in polymer‐binder‐modified cementitious matrix for SHM applications. These e‐textile embedded cementitious composites are independent of any percolation threshold due to the interconnected fabric inside the host matrix. The piezo‐resistive response was measured by applying direct and cyclic compressive loads (ranging from 0.10 to 3.90 MPa). A pressure sensitivity of 1.50 MPa−1 and an ultra‐high gauge factor of 2000 was obtained for the system of the self‐sensing cementitious structure with embedded e‐textiles. The sensitivity of this new system with embedded e‐textile is an order of magnitude higher than the state‐of‐the‐art nanoparticle based self‐sensing cementitious composites. The composites showed mechanical stability and functional durability over long‐term cyclic compression tests of 1000 cycles. Additionally, a two time‐constant model was used to validate the experimental results on decay response of the e‐textile embedded composites

Identifying Algal Bloom ‘Hotspots’ in Marginal Productive Seas: A Review and Geospatial Analysis

Algal blooms in the marginal productive seas of the Indian Ocean are projected to become more prevalent over the coming decades. They reach from lower latitudes up to the coast of the northern Indian Ocean and the populated areas along the Arabian Gulf, Sea of Oman, Arabian Sea, and the Red Sea. Studies that document algal blooms in the Indian Ocean have either focused on individual or regional waters or have been limited by a lack of long-term observations. Herein, we attempt to review the impact of major monsoons on algal blooms in the region and identify the most important oceanic and atmospheric processes that trigger them. The analysis is carried out using a comprehensive dataset collected from many studies focusing on the Indian Ocean. For the first time, we identify ten algal bloom hotspots and identify the primary drivers supporting algal blooms in them. Growth is found to depend on nutrients brought by dust, river runoff, upwelling, mixing, and advection, together with the availability of light, all being modulated by the phase of the monsoon. We also find that sunlight and dust deposition are strong predictors of algal bloom species and are essential for understanding marine biodiversity

Identifying Algal Bloom &lsquo;Hotspots&rsquo; in Marginal Productive Seas: A Review and Geospatial Analysis

Algal blooms in the marginal productive seas of the Indian Ocean are projected to become more prevalent over the coming decades. They reach from lower latitudes up to the coast of the northern Indian Ocean and the populated areas along the Arabian Gulf, Sea of Oman, Arabian Sea, and the Red Sea. Studies that document algal blooms in the Indian Ocean have either focused on individual or regional waters or have been limited by a lack of long-term observations. Herein, we attempt to review the impact of major monsoons on algal blooms in the region and identify the most important oceanic and atmospheric processes that trigger them. The analysis is carried out using a comprehensive dataset collected from many studies focusing on the Indian Ocean. For the first time, we identify ten algal bloom hotspots and identify the primary drivers supporting algal blooms in them. Growth is found to depend on nutrients brought by dust, river runoff, upwelling, mixing, and advection, together with the availability of light, all being modulated by the phase of the monsoon. We also find that sunlight and dust deposition are strong predictors of algal bloom species and are essential for understanding marine biodiversity

Identifying Algal Bloom ‘Hotspots’ in Marginal Productive Seas: A Review and Geospatial Analysis

Algal blooms in the marginal productive seas of the Indian Ocean are projected to become more prevalent over the coming decades. They reach from lower latitudes up to the coast of the northern Indian Ocean and the populated areas along the Arabian Gulf, Sea of Oman, Arabian Sea, and the Red Sea. Studies that document algal blooms in the Indian Ocean have either focused on individual or regional waters or have been limited by a lack of long-term observations. Herein, we attempt to review the impact of major monsoons on algal blooms in the region and identify the most important oceanic and atmospheric processes that trigger them. The analysis is carried out using a comprehensive dataset collected from many studies focusing on the Indian Ocean. For the first time, we identify ten algal bloom hotspots and identify the primary drivers supporting algal blooms in them. Growth is found to depend on nutrients brought by dust, river runoff, upwelling, mixing, and advection, together with the availability of light, all being modulated by the phase of the monsoon. We also find that sunlight and dust deposition are strong predictors of algal bloom species and are essential for understanding marine biodiversity.</jats:p

Colossal figure of merit and compelling HER catalytic activity of holey graphyne

Abstract Herein, we have conducted a comprehensive study to uncover the thermal transport properties and hydrogen evolution reaction catalytic activity of recently synthesized holey graphyne. Our findings disclose that holey graphyne has a direct bandgap of 1.00 eV using the HSE06 exchange–correlation functional. The absence of imaginary phonon frequencies in the phonon dispersion ensures its dynamic stability. The formation energy of holey graphyne turns out to be − 8.46 eV/atom, comparable to graphene (− 9.22 eV/atom) and h-BN (− 8.80 eV/atom). At 300 K, the Seebeck coefficient is as high as 700 μV/K at a carrier concentration of 1 × 1010 cm-2. The predicted room temperature lattice thermal conductivity (κl) of 29.3 W/mK is substantially lower than graphene (3000 W/mK) and fourfold smaller than C3N (128 W/mK). At around 335 nm thickness, the room temperature κl suppresses by 25%. The calculated p-type figure of merit (ZT) reaches a maximum of 1.50 at 300 K, higher than that of holey graphene (ZT = 1.13), γ-graphyne (ZT = 0.48), and pristine graphene (ZT = 0.55 × 10–3). It further scales up to 3.36 at 600 K. Such colossal ZT values make holey graphyne an appealing p-type thermoelectric material. Besides that, holey graphyne is a potential HER catalyst with a low overpotential of 0.20 eV, which further reduces to 0.03 eV at 2% compressive strain

Colossal figure of merit and compelling HER catalytic activity of holey graphyne.

Acknowledgements: N.S acknowledges the financial support from Khalifa University of Science and Technology under the Emerging Science & Innovation Grant ESIG-2023-004. The authors also acknowledge the contribution of Khalifa University’s high-performance computing and research computing facilities to the results of this research.Herein, we have conducted a comprehensive study to uncover the thermal transport properties and hydrogen evolution reaction catalytic activity of recently synthesized holey graphyne. Our findings disclose that holey graphyne has a direct bandgap of 1.00 eV using the HSE06 exchange-correlation functional. The absence of imaginary phonon frequencies in the phonon dispersion ensures its dynamic stability. The formation energy of holey graphyne turns out to be - 8.46 eV/atom, comparable to graphene (- 9.22 eV/atom) and h-BN (- 8.80 eV/atom). At 300 K, the Seebeck coefficient is as high as 700 μV/K at a carrier concentration of 1 × 1010 cm-2. The predicted room temperature lattice thermal conductivity (κl) of 29.3 W/mK is substantially lower than graphene (3000 W/mK) and fourfold smaller than C3N (128 W/mK). At around 335 nm thickness, the room temperature κl suppresses by 25%. The calculated p-type figure of merit (ZT) reaches a maximum of 1.50 at 300 K, higher than that of holey graphene (ZT = 1.13), γ-graphyne (ZT = 0.48), and pristine graphene (ZT = 0.55 × 10-3). It further scales up to 3.36 at 600 K. Such colossal ZT values make holey graphyne an appealing p-type thermoelectric material. Besides that, holey graphyne is a potential HER catalyst with a low overpotential of 0.20 eV, which further reduces to 0.03 eV at 2% compressive strain

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

AbstractMaterials 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 (&lt; 25 cm2)—on-body personal exposure monitoring.</jats:p

The effect of the ultrasonication pre-treatment of graphene oxide (GO) on the mechanical properties of GO/polyvinyl alcohol composites

The effects of ultrasonication pre-treatment of graphene oxide (GO) on the mechanical properties of GO/poly(vinyl alcohol) (PVA) composites were studied. Results show that the mechanical properties are sensitive to ultrasonication time (or energy input) and there is an optimum ultrasonication time (OUT) that leads to the largest improvement in mechanical property. If the ultrasonication time is less than OUT, the GO sheets are not fully exfoliated, and only a partial reinforcement effect is achieved. Ultrasonication times longer than OUT cause a reduction of GO sheet size and impair the efficiency of mechanical improvement. The optimized ultrasonication energy input of 15 W h/L may serve as a general guideline for preparation of GO composites