Dysprosium doped copper oxide (Cu1-xDyxO) nanoparticles enabled bifunctional electrode for overall water splitting

The production of hydrogen, a favourable alternative to an unsustainable fossil fuel remains as a significant hurdle with the pertaining challenge in the design of proficient, highly productive and sustainable electrocatalyst for both oxygen evolution reaction (OER) and hydrogen evolution reaction (HER). Herein, the dysprosium (Dy) doped copper oxide (Cu_1-xDy_xO) nanoparticles were synthesized via solution combustion technique and utilized as a non-noble metal based bi-functional electrocatalyst for overall water splitting. Due to the improved surface to volume ratio and conductivity, the optimized Cu_1-xDy_xO (x = 0.01, 0.02) electrocatalysts exhibited impressive HER and OER performance respectively in 1 M KOH delivering a current density of 10 mAcm^?2 at a potential of ?0.18 V vs RHE for HER and 1.53 V vs RHE for OER. Moreover, the Dy doped CuO electrocatalyst used as a bi-functional catalyst for overall water splitting achieved a potential of 1.56 V at a current density 10 mAcm^?2 and relatively high current density of 66 mAcm^?2 at a peak potential of 2 V. A long term stability of 24 h was achieved for a cell voltage of 2.2 V at a constant current density of 30 mAcm^?2 with only 10% of the initial current loss. This showcases the accumulative opportunity of dysprosium as a dopant in CuO nanoparticles for fabricating a highly effective and low-cost bi-functional electrocatalyst for overall water splitting.     

Investigation on sulphonated zinc oxide nanorod incorporated sulphonated poly (1,4-phenylene ether ether sulfone) nanocomposite membranes for improved performance of microbial fuel cell

Hydrothermally prepared zinc oxide nanorods are sulphonated (S–ZnO NR) and incorporated into 15% Sulphonated Poly (1,4-Phenylene Ether Ether Sulfone) (SPEES) to improve the hydrophilicity, water uptake and ion transfer capacity. Water uptake and ion transfer capacity increased to 34.6 ± 0.6% and 2.0 ± 0.05 meq g^?1 from 29.8 ± 0.3% and 1.4 ± 0.04 meq g^?1 by adding 7.5 wt% S–ZnO NR to SPEES. Morphological studies show the prepared S–ZnO NR is well dispersed in the polymer matrix. SPEES +7.5 wt% S–ZnO NR membrane exhibits optimum performance after three-weeks of continual operation in a fabricated microbial fuel cell (MFC) to produce a maximum power density of 142 ± 1.2 mW m^?2 with a reduced biofilm compared to plain SPEES (59 ± 0.8 mW m^?2), unsulphonated filler incorporated SPEES (SPEES + 7.5 wt% ZnO, 68 ± 1.1 mW m^?2) and Nafion (130 ± 1.5 mW m^?2) thereby suggesting its suitability as a sustainable and improved cation exchange membrane (CEM) for MFCs.     

Microstructure evolution during batch annealing

Design of industrial annealing cycles requires recrystallization and grain growth studies, which are typically carried out under isothermal laboratory condition. The kinetics coefficients of these phase transformations are obtained from such studies, which are subsequently used in designing the industrial nonisothermal cycles using the additivity principles. However, the strong heating rate effects on the grain growth kinetics necessitate such kinetics studies using industrial thermal profiles. In the present work, the hot and cold spot cycles of an industrial batch annealing cycle for AIK grade steel have been simulated in a programmable laboratory furnace. Subsequently, the effect of annealing temperature, soaking time, and heating rate on the microstructural features, such as grain size distribution, grain shape anisotropy, and grain orientation, have been investigated through extensive quantitative microscopy. The implications of these results on the design of industrial batch annealing cycles have been discussed.     

Performance of orthogonal frequency division multiplexing based M-ary quadrature amplitude modulation in asymmetrical dual-hop mixed RF-FSO transmission system

This paper investigates the performance of a dual-hop mixed relay system with radio frequency (RF) and free-space optics (FSO) communication under the effect of pointing error (PE) and atmospheric turbulence (AT). This paper considers a system where RF and FSO links are cascaded. The RF link is modeled by Nakagami-m fading, and the FSO link is modeled as gamma–gamma (G-G) fading channel. Both the channel models use orthogonal frequency division multiplexing (OFDM) with M-ary quadrature amplitude modulation (QAM). The expressions for probability density function, cumulative distribution function, signal-to-noise ratio, and ergodic capacity are derived. The moment generating function (MGF) of fading and the bit error rate (BER) of the OFDM-based M-ary QAM scheme is derived in terms of Meijer's G-function. It has been observed that, in fixed gain relay systems, the modulation scheme's BER is dominated by the SNR of the RF link. While in a variable gain relay system, the turbulence conditions of the FSO system affect the SNR and the BER of the modulation method. The feasibility of heterodyne detection and intensity modulation direct detection (IM/DD) is analyzed in terms of outage probability and ergodic capacity. The results can be used to choose the optimal modulation order and relay system for QAM-OFDM-based optical wireless systems.     

A Consortium Blockchain Based Overseas Fund Transfer System

Domestic fund transfers are all the time immediate, handier and obtainable however in overseas fund transfer, funds are transferred from payers account to payees account through the correspondent banks, central bank and messaging network which involves intermediaries and allied charges. Blockchain Technology has grabbed the interest of financial systems due to its inventive benefits such as decentralised structure, faster speed, cost-effectiveness and more security over conventional approaches. Adopting Blockchain Technology for overseas fund transfer can overcome the transaction delays, intermediaries’ failures and recurring costs involved in the current system. In this paper, a Consortium Blockchain based overseas fund transfer process is proposed to achieve faster operations, security and transparency. Hyperledger Fabric Blockchain framework has been used for overseas fund transfer process and web-user interface is developed for the same. Simulation results demonstrate the significance of Blockchain as a promising technology for overseas fund transfer process to facilitate speed, security, transparency and efficiency for overseas fund transfer.

     

AH-64D main transmission accessory drive spur gear installation fault detections

The US Army has launched an aggressive program to implement condition based maintenance on its rotary wing assets. Condition Based Maintenance takes advantage of technology developments in the areas of machinery monitoring, signal processing and fault modeling to reduce the cost of ownership through improved maintenance procedures. Documenting the reduction in maintenance burden, cost savings and increased safety through early detection of helicopter faults is an important step in justifying the program. This paper describes a novel technique used to detect a serious fault in the accessory section of the AH-64D Apache main transmission. The technique determines if the primary or secondary clutch system is driving the aircraft accessories through a tachometer speed ratio. The method has identified three aircraft operating on the secondary system allowing Army maintenance crews to replace the affected transmissions and quickly restore the aircraft to operational status.     

Antifungal activity of five different essential oils in vapour phase for the control of Colletotrichum gloeosporioides and Lasiodiplodia theobromae in vitro and on mango

Mango fruit has high commercial value; however, major postharvest losses are encountered throughout the supply chain due to postharvest diseases. These results lead to the search for natural fungicide for postharvest diseases control. The antifungal effects of five essential oils (thyme, clove, cinnamon, anise and vitex) were assessed by disc volatilisation method. Thyme oil vapours at 5 μL per Petriplate, and clove and cinnamon oil at 8 μL per Petriplate showed 100% growth inhibition of mango pathogens in vitro. GC/MS analysis of essential oil showed thymol (23.88), o‐cymol (23.88) and terpinolene (23.88) as the major constituents of thyme oil. Clove and cinnamon oils contain 3‐allyl‐2‐methoxyphenol (37.42%) and benzofuran 3‐methyl (17.97%), respectively. Thyme oil as a fumigant at 66.7 μL L^?1 showed a significant (P < 0.05) inhibition on postharvest pathogens of mango fruits stored at 25 °C for 6 days. Results of our study suggest the possibility of using thyme oil as an alternate natural fungicide to manage postharvest diseases in mango.     

Enhancing the efficiency of alternating current driven organic light-emitting devices by optimizing the operation frequency

We report efficient and bright organic light-emitting devices operated by capacitive energy coupling. In this approach, the organic layers are enclosed between sputter-deposited hafnium dioxide layers to prevent charge carrier injection. When a sinusoidal voltage signal is applied to the electrodes, the devices emit bright green light whereas no detectable emission is generated upon application of a constant voltage. The efficiency of the process depends heavily on the frequency of the applied voltage signal. By optimizing the driving scheme, a record luminous efficacy for AC driven OLEDs of 2.7 lm/W at 500 cd/m² is achieved.     

Understand Students Feedback Using Bi-Integrated CRF Model Based Target Extraction

Educational institutions showing interest to find the opinion of the students about their course and the instructors to enhance the teaching-learning process. For this, most research uses sentiment analysis to track students’ behavior. Traditional sentence-level sentiment analysis focuses on the whole sentence sentiment. Previous studies show that the sentiments alone are not enough to observe the feeling of the students because different words express different sentiments in a sentence. There is a need to extract the targets in a given sentence which helps to find the sentiment towards those targets. Target extraction is the subtask of targeted sentiment analysis. In this paper, we proposed the innovative model to find the targets of the given sentence using Bi-Integrated Conditional Random Fields (CRF). A Parallel fusion neural network model is designed to perform this task. We evaluate the model using the Michigan dataset and we build a dataset for target extraction from student reviews. The experimental results show that our proposed fusion model achieves better results compared to baseline models.