Search

Ultrathin transparent conductive oxides (TCOs) are emerging candidates for next-generation transparent electronics. Indium oxide (In2O3) incorporated with post-transition-metal ions (e.g., Sn) has been widely studied due to their excellent optical transparency and electrical conductivity. However, their electron transport properties are deteriorated at the ultrathin two-dimensional (2D) morphology compared to that of intrinsic In2O3. Here, we explore the domain of transition-metal dopants in ultrathin In2O3 with the thicknesses down to the single-unit-cell limit, which is realized in a large area using a low-temperature liquid metal printing technique. Zn dopant is selected as a representative to incorporate into the In2O3 rhombohedral crystal framework, which results in the gradual transition of the host to quasimetallic. While the optical transmittance is maintained above 98%, an electron field-effect mobility of up to 87 cm2 V-1 s-1 and a considerable sub-kΩ-1 cm-1 ranged electrical conductivity are achieved when the Zn doping level is optimized, which are in a combination significantly improved compared to those of reported ultrathin TCOs. This work presents various opportunities for developing high-performance flexible transparent electronics based on emerging ultrathin TCO candidates.

On-chip optical switches have emerged as a new class of photonic components for high-performance optical communication networks and on-chip interconnects, in which the all-optical configuration without the incorporation of other control-means is highly desired. While two-dimensional (2D) ultrathin materials demonstrate their great potential in developing ultrafast all-optical switches owing to their unique light–matter interaction, such investigations have so far been limited to the fiber optic platform or free space. Here, we realize an all-optical on-chip switch from a silicon waveguide-based asymmetric Mach–Zehnder interferometer (MZI) structure enabled by 2D ultrathin Ga2S3. Upon the visible light excitation at 532 nm, excessive photocarriers in Ga2S3 cause a change of the refractive index and subsequently a phase variation between MZI arms at the 1550 nm operation wavelength, triggering on the optical switch. On the other hand, the switch is off without the visible light stimulation, as the phase variation is recovered due to the ultrafast photo-exciton relaxation behavior of Ga2S3. The Ga2S3-enabled all-optical switch is driven at an extremely small optical power density of 0.12 W cm−2 and exhibits a response and recovery time of 26.3 and 43.5 μs, respectively, which as a combination is superior to those of fiber optic-based all-optical switches enabled by 2D materials. This work may provide a viable approach to develop on-chip all-optical photonic components for practical integrated photonic chips.

Owing to their short communication range, wireless nodes in wireless sensor networks (WSNs) can exchange information with devices in their vicinity only. Thus, in sparse networks, the full connectivity of the network is rarely achieved. This renders a centralised approach towards localisation in WSNs useless. Moreover, the exploitation of a centralised algorithm for localisation compromises the scalability in dense networks. Thus, a decentralised, location-aware network with partial connectivity and hybrid (range and direction) measurements obtained between known sensors (reference sensors) and sensors at unknown locations (target sensors) is under focus. The decentralised location estimation is obtained using a linear least squares (LLS) approach and performance enhancements are achieved by introducing a weighing strategy to produce weighted least squares (WLS) estimates. This distributed estimation is made possible by designing a map stitching technique that forms the global map of the network from individual local maps of the wireless nodes without compromising the distributed nature of the network. In the analytical section of the study, theoretical mean squares error expression is derived for LLS estimation, and a Cramer–Rao lower bound is derived to bind the performance of the WLS solution. The algorithm's performance validation is conducted both theoretically and via simulations.

The library of true two-dimensional materials is limited since many transition metal compounds are not stratified and can thus not be easily isolated as nanosheets. Here, micron-sized ultrathin rutile TiO2 nanosheets featuring uniform thickness (2 ± 0.5 nm) with dielectric constant (e⊥ = 24) have been synthesized via a liquid metal synthesis strategy.

Facile and scalable synthesis of two-dimensional (2D) non-layered materials is highly desirable for novel applications. Iron compounds including hematite (α-Fe2O3) and iron phosphide (FeP) are an important group of semiconductors but their 2D ultrathin morphology has been rarely reported. Here, we develop a water dissolvable template-based synthesis route to produce free-standing ultrathin iron compounds. Such a method also enables the tunability of morphology from mesoporous nanosheet to meso-macroporous hierarchical nanonet utilizing the aging process, while its corresponding surface active area is reduced simultaneously. Ultrathin hematite is relatively inert to electrochemical hydrogen evolution reaction (HER). However, FeP exhibits excellent catalytic performances with a relatively low overpotential of 117 mV and a Tafel slope of 56 mV dec−1, which is as a whole improved over those of reported free-standing binary transition-metal phosphide nanostructures. This work extends the possibility to produce high-quality 2D non-layered materials, which are expected to exhibit unique properties compared to their bulk counterparts.

Smart Grid (SG) operation requires multi-way communication among power generation, transmission, distribution, and consumer facilities. The complete SG architecture involves multiple layers of networks stretching from energy control center to consumer facilities and multiple applications with diverse Quality-of-Service (QoS) requirements. To meet these requirements, Cognitive Radio (CR) which offers efficient spectrum utilization and sharing, has been considered as a favorable solution. Due to diverse requirements of latency, data rate and reliability, it is a great challenge to support various SG applications under spectrum sensing uncertainties, Signal-to-Noise Ratio (SNR) variations and availability of desired channel bandwidth. In this paper, we propose a dynamic channel selection algorithm based on Fuzzy Inference System (FIS) capable of selecting the most appropriate available idle channel with desired bandwidth, minimum required SNR and probability of miss detection Pmd. The proposed algorithm utilizes various modes of IEEE Standard 802.22, Wireless Regional Area Network (WRAN), at the physical layer. In case of unavailability of idle channels with required SNR and/or Pmd, we propose a novel approach to select the channel with closest possible parameters aiming to reduce the re-transmission probability and thus achieve the desired throughput, latency and reliability requirements. The performance is compared with existing algorithms to demonstrate the effectiveness and superiority of the proposed algorithm.

A genuine concern faced by the present world is global warming. Millions of human and animal lives are at risk due to global warming. Therefore, the subject has gained enormous attention from research and academia around the world. Literature shows that the primary cause of global climate change or global warming is carbon (CO2) emissions. Hence, the role of a reliable carbon emission measurement is important for devising a relevant climate policy to deal with environmental problems. Based on trade-adjusted statistics of carbon emissions, a relevant climate policy response can be provided. Unlike the previous studies, this study examines the asymmetric impact of international trade on consumption-based carbon emissions from 1990 to 2017 in the G7 economies. To get empirical estimates, the study applies second-generation co-integration technique and nonlinear panel autoregressive distributive lag (NPARDL) model for estimating the relevant coefficients. The empirical results show that positive growth of exports significantly decreases consumption-based carbon emissions both in the short and long run, whereas the impact of negative growth of exports is insignificant. For imports, the results show that, over time, positive growth of imports significantly increases consumption-based carbon emissions in the long run, while the impact of negative growth of imports is insignificant. Finally, it is recommended for the policymakers to target the export industries for relevant policy interventions, which are less polluting and can generate other economic benefits as well.

Phosphides of transition metals (TMPs) are a developing class of materials for hydrogen evolution reaction (HER) as an alternative to expensive noble metals to produce clean energy. Herein, the nitrogen-doped molybdenum oxide (MoOx) is developed via a facile and simple hydrothermal method, followed by annealing in the N2 atmosphere and phosphorization to form a nitrogen-doped oxygenated molybdenum phosphide (N-MoP) sphere-shaped structure. The developed N-doped phosphide structure depicts enhanced HER activity by reaching a current density of 10 mA cm-2 at a very low overpotential of only 87 mV, which is much better than annealed nitrogen-doped molybdenum oxide (A-MoOx) 138 mV in alkaline medium. N-MoP is a highly efficient electrocatalyst for HER attributed to a more exposed surface, large electrode/electrolyte interface and appropriate binding energies for reactants. This study extends the opportunity of developing nitrogen-doped TMPs, which can display exceptional properties as compared to their oxides.

The development of sodium and potassium ion batteries (SIBs/KIBs) has seen tremendous growth in recent years due to their promising properties as a potential replacement for lithium-ion batteries (LIBs). Here, we report ultrafine iron selenide (Fe3Se4) nanoparticles embedded into one-dimensional (1D) carbon fibers (Fe3Se4@CFs) as a potential candidate for SIBs/KIBs. The Fe-based metal-organic framework particles (MOFP) are used as a Fe source to obtain highly dispersed Fe3Se4 nanoparticles in the product. The Fe3Se4@CF consisted of ultrafine particles of Fe3Se4 with an average particle size of ~10 nm loaded into CFs with an average diameter of 300 nm. The product exhibited excellent specific activity of ~439 and ~435 mAh/g at the current density of 50 mA/g for SIBs and KIBs, respectively. In addition, the as-prepared anodes (Fe3Se4@CFs) exhibited excellent capacity retention up to several hundred cycles (700 cycles for SIBs and 300 cycles for KIBs). The high activity and excellent stability of the developed electrodes make Fe3Se4@CFs a promising electrode for next-generation batteries.

A novel and distributed version of locally linear embedding (LLE) for the relative positioning of sensor nodes in wireless sensor networks is under focus. The proposed algorithm is iterative in nature and exploits the range and bearing estimates between a sensor and its $k$ -nearest neighbors forming a local neighborhood that is then utilized in a cooperative fashion to map the whole network. As a result, a partially connected network is localized with every sensor having an estimate of all the sensor positions in the network. It is shown that the proposed distributed mechanism of LLE does not compromise the accuracy of estimation when compared with conventional centralized LLE.

Degradation of natural organic matter especially Humic acid is a big challenge, while its byproducts are carcinogenic, which is a threat to life. Here, Fe3+:ZnO has been deployed on ceramic plats (CP) using SILAR method for solar photocatalytic mineralization of HA in municipal wastewater. Structural results show that ZnO and Fe3+:ZnO deployed over CP have an average crystallite size of 47.43 nm and 29.54 nm, respectively. Further, Fe3+ doping increased the conductivity of ZnO due to enhanced photon capturing ability and effective surface area. Almost 90% HA photodegradation was achieved at the reaction parameters optimized by response surface methodology. Owing to the strong interaction between negatively charged HA and positive zeta potential of ZnO@CP (24 mV) and Fe3+:ZnO@CP (26 mV). Moreover, a significant reduction in BOD (~73%), COD (~76%) and TOC (~63%) have been observed after solar photocatalytic treatment of municipal wastewater. In comparison, ZnO@CP hybrid removed HA (400 mgL−1) at the rate of 3.3 kgm−3d−1 with a lower reduction in BOD (~57%), COD (~45%) and TOC (~57%) than Fe3+:ZnO@CP, confirming that the Fe doping has tuned the bandgap and manipulated the active sites onto the surface of ZnO. Moreover, direct coating of photocatalyst over CP provided exceptional stability and reusability as a photocatalyst which maintained ~80% of its activity even after 14 cycles of wastewater treatment and only lose 29% of its activity after 22 cycles. Hence, by designing a photocatalyst adhered directly onto the ceramic (substrate) present the novel sustainable photocatalyst supported wastewater treatment systems at a large scale.

The use of leaded gasoline adversely affects cardiovascular, nervous, and immune systems. Study projects to rule out different variables of prognostic importance in lead-exposed subjects. A total of 317 traffic wardens with 5 years of outdoor experience and Hb levels

Silicon photonics has demonstrated great potential in ultrasensitive biochemical sensing. However, it is challenging for such sensors to detect small ions which are also of great importance in many biochemical processes. A silicon photonic ion sensor enabled by an ionic dopant-driven plasmonic material is introduced here. The sensor consists of a microring resonator (MRR) coupled with a 2D restacked layer of near-infrared plasmonic molybdenum oxide. When the 2D plasmonic layer interacts with ions from the environment, a strong change in the refractive index results in a shift in the MRR resonance wavelength and simultaneously the alteration of plasmonic absorption leads to the modulation of MRR transmission power, hence generating dual sensing outputs which is unique to other optical ion sensors. Proof-of-concept via a pH sensing model is demonstrated, showing up to 7 orders improvement in sensitivity per unit area across the range from 1 to 13 compared to those of other optical pH sensors. This platform offers the unique potential for ultrasensitive and robust measurement of changes in ionic environment, generating new modalities for on-chip chemical sensors in the micro/nanoscale.

Identifying precursor events that allow the timely forecasting of landslides, thereby enabling risk reduction, is inherently difficult. Here we present a novel, low cost, flow visualization technique using time-lapsed imagery (TLI) that allows real time analysis of slope movement. This approach is applied to the Rest and Be Thankful slope, Argyle, Scotland, where past debris flows have blocked the A83 or forced preemptive closure. TLI of the Rest and Be Thankful are taken from a fixed station, 28 mm lens, time lapse camera every 15 min. Imagery is filtered to counter the effects of misalignment from wind induced vibration of the camera, asymmetric lighting, and fog. Particle image velocimetry (PIV) algorithms are then run to produce slope movement velocity vectors. PIV generated vectors are automatically post-processed to separate vectors generated by slope movement from false positives generated by harsh environmental conditions. Results for images over a 20-day period indicated precursor slope movement initiated by a rainfall event, a period of quiescence for 10 days, followed by a large landslide failure during proceeding rainfall where over 3000 tons of sediment reached the road. Results suggest low cost, live streamed TLI and this novel PIV approach correctly detect and, importantly, report precursor slope movement, allowing early warning, effective management and landslide impact mitigation. Future applications of this technique will allow the development of an effective decision-making tool for asset management of the A83, reducing the risk to life of motorists. The technique can also be applied to other critical infrastructure sites, allowing hazard risk reduction.

To meet the diverse QoS requirements of future Smart Grid (SG) communication network, efficient traffic scheduling and optimization techniques to prioritize data from various SG applications are required. Recently, focus has only been on the latency and criticality based priority-aware policies for same channel bandwidth. Traffic scheduling and subsequent channel allocation are required to support both the differential throughput and latency requirements simultaneously for channels having different bandwidths and SNRs. In this paper, a QoS-aware framework for data traffic scheduling in cognitive radio based SG communication network is proposed. The channels available to smart grid Communication Nodes (SCNs) are categorized as low and high bandwidths. For each bandwidth, all SG applications are categorized into several priority-classes comprised of latency and throughput sub-classes. For both channel bandwidths, the scheduler maintains and updates two sets of priority queues based on weights associated with each class and its data type. The complete scheduling framework is formulated as a multi-objective optimization problem. The overall objective function is the weighted sum of individual utility functions of latency and throughput. A novel usage of Adam optimizer is proposed to minimize the latency and maximize the throughput by obtaining optimal system cost, resulting in optimal decision policy. Simulation results show that the proposed algorithm achieves desired QoS requirements in the presence of heavy PU traffic, whereas in case of no priority assignment, QoS requirements of lower priority applications are met by compromising the QoS of higher priority data.

In this study, the authors introduce a novel hybrid cooperative localisation scheme when both distance and angle measurements are available. Two linear least squares (LLS) hybrid cooperative schemes based on angle of arrival–time of arrival (AoA–ToA) and AoA–received signal strength (AoA–RSS) signals are proposed. The proposed algorithms are modified to accommodate cooperative localisation in resource constrained networks where only distance measurements are available between target sensors (TSs) while both distance and angle measurements are available between reference sensors and TSs. Furthermore, an optimised version of the LLS estimator is proposed to further enhance the localisation performance. Moreover, localisation of sensor nodes in networks with limited connectivity (partially connected networks) is also investigated. Finally, computational complexity analysis of the proposed algorithms is presented. Through simulation, the superior performance of the proposed algorithms over its non-cooperative counterpart and the hybrid signal based iterative non-linear least squares algorithms is demonstrated.

The present study is aimed to investigate the phytochemical screening and biological activities of methanolic extract of Cyperus scariosus roots. Dried plant was grounded and extracted with methanol to prepare methanol crud extract. In vitro biological tests were conducted using this methanolic extracts according to the standard procedure. 100% death rate of brine shrimp was perceived at 3 mg/mL of plant extract after 72 hours. The extract showed action against Aspergillus flavius i.e. 90% followed by A. niger (91%) while the highest activity was shown against A. fumegatrus (94%). Important scavenging results were detected during scavenging of free radicals viz; 92.2% against DPPH, 82.2% to ABTS, 75.8% to hydrogen peroxide, 88.1% to β-carotene, 86.1% to hydroxyl radical and 89.4% against phosphomolybdate at 3 mg/mL were obtained. The results obtained in this study point out that extract showed significant biological activities which might be due to the presence of bioactive constituents.