CO2 assisted synthesis of silk fibroin driven robust N-doped carbon aerogels coupled with nickel–cobalt particles as highly active electrocatalysts for HER

Hydrogen evolution reaction (HER), has been considered as an ideal alternative energy system to alleviate the global energy crisis. Unfortunately, the rational design of a cost-effective, highly active and stable electrocatalyst remains a tough challenge. In this work, a CO₂ assisted synthesis of N- doped carbon aerogel derived from silk fibroin (SFCA) without any additives was used to anchor Nickel–Cobalt (NiCo) nanoparticles as an HER electrocatalyst in basic medium (1 M KOH). The obtained SFCA-NiCo electrocatalyst demonstrated outstanding electrochemical performance having an onset potential of 52 mV and a low overpotential of 179 mV at current density of 10 mA cm^?2, whilst exhibiting long term stability when tested for 24 h. The outstanding electrocatalytic performance marks SFCA-NiCo as a favourable electrocatalyst for HER and the synthesis strategy paves way for constructing novel catalysts even for other areas.     

Changes in evaporative emissions from gasoline in the Nigeria market

The evaporative emissions from multicomponent fuel are expected to change significantly with the progress of evaporation. This study presents the findings of air emission associated with changes (increase) in evaporation process of gasoline. The parameters measured in the experiment included the weight loss, density, viscosity of gasoline, and the evaporation rate. Results showed that the parameters changed significantly as evaporation progressed. The weight loss was from 3.02% to 5.45% after 30 m and presented a logarithmic curve with time. The density measurements of the gasoline samples were 728.5–746.7 kg/m³. The evaporation rate which was 1.22 × 10^?5–2.14 × 10^?5 kg/s showed great increase with decrease in density and kinematic viscosity at ambient temperature with sample C (from Lagos) having the highest evaporation rate. The emission rate was observed to be inversely proportional to the sample density. Most of the changes might be attributed to the air emission of volatile hydrocarbons such as benzene, toluene, ethyl benzene, and xylene (BTEX) from gasoline surface to the environment.     

Evolutionary multi criteria design optimization of robot grippers

This paper explores the use of intelligent techniques to obtain optimum geometrical dimensions of a robot gripper. The optimization problem considered is a non-linear, complex, multi-constraint and multicriterion one. Three robot gripper configurations are optimized. The aim is to find Pareto optimal front for a problem that has five objective functions, nine constraints and seven variables. The problem is divided into three cases. Case 1 has first two objective functions, the case 2 considers last three objective functions and case 3 deals all the five objective functions. Intelligent optimization algorithms namely Multi-objective Genetic Algorithm (MOGA), Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-objective Differential Evolution (MODE) are proposed to solve the problem. Normalized weighting objective functions method is used to select the best optimal solution from Pareto optimal front. Two multi-objective performance measures (solution spread measure (SSM) and ratio of non-dominated individuals (RNIs)) are used to evaluate the strength of the Pareto optimal fronts. Two more multi-objective performance measures namely optimizer overhead (OO) and algorithm effort are used to find the computational effort of MOGA, NSGA-II and MODE algorithms. The Pareto optimal fronts and results obtained from various techniques are compared and analyzed.     

Effective layer-based segmentation of compound images using morphology

Effective compound image compression algorithms require compound images to be first segmented into regions such as text, pictures and background to minimize the loss of visual quality of text during compression. In this paper, a new compound image segmentation algorithm based on the Mixed Raster Content model (MRC) of multilayer approach is proposed (foreground/mask/background). This algorithm first segments a compound image into different classes. Then each class is transformed to the three-layer MRC model differently according to the property of that class. Finally, the foreground and the background layers are compressed using JPEG 2000. The mask layer is compressed using JBIG2. The proposed morphological-based segmentation algorithm design a binary segmentation mask which partitions a compound image into different layers, such as the background layer and the foreground layer accurately. Experimental results show that it is more robust with respect to the font size, style, colour, orientation, and alignment of text in an uneven background. At similar bit rates, our MRC compression with the morphology-based segmentation achieves a much higher subjective quality and coding efficiency than the state-of-the-art compression algorithms, such as JPEG, JPEG 2000 and H.264/AVC-I.     

Impact of enzyme preparation and degree of hydrolysis on peptide profile and nitrogen solubility of sodium caseinate hydrolysates

The solubility of casein ingredients is limited under certain pH conditions. Therefore, sodium caseinate (NaCN) was hydrolysed at laboratory scale with four enzyme preparations (Alcalase, Prolyve, FlavorPro Whey and pepsin) yielding hydrolysates having degree of hydrolysis (DH) values between 0.19 ± 0.11 and 19.25 ± 0.73%. The nitrogen solubility index (NSI) over the pH range 2.0–8.0 was affected by (i) the specificity of the enzyme preparation, (ii) the DH and (iii) the presence of unhydrolysed NaCN within the hydrolysate. The trends observed at laboratory scale (<5 L) were also seen with the semi‐pilot‐scale (300 L) hydrolysates generated with Alcalase and FlavorPro Whey. Removal of unhydrolysed NaCN from the FlavorPro Whey hydrolysate using a 5‐kDa ultrafiltration (UF) membrane increased NSI. This study has highlighted the importance of carefully selecting the proteolytic preparation along with the DH to design casein‐based ingredients with enhanced technofunctional properties.     

Antimicrobial interactions of microbial species involved in the fermentation of cassava dough into agbelima with particular reference to the inhibitory effect of lactic acid bacteria on enteric pathogens

Lactic acid bacteria, Bacillus species and yeasts are involved in the fermentation of cassava dough into agbelima. Microbial interactions within and between these groups of microorganisms were investigated in addition to the survival of five enteric pathogens inoculated into agbelima under various conditions. Nine out of 10 cultures of lactic acid bacteria isolated at the end of agbelima fermentation showed inhibitory effect against 10 cultures of lactic acid bacteria isolated at the start of fermentation. Only 3 out of 10 isolates of Bacillus subtilis were inhibited by 10 isolates of lactic acid bacteria tested. No interactions were observed between yeasts and the lactic acid bacteria, whereas three of the Bacillus isolates showed inhibitory effects against the yeasts. Twelve isolates of Lactobacillus plantarum tested inhibited the growth of an isolate each of Lactobacillus fermentum and Lactobacillus brevis but none tested positive for bacteriocin production. The antimicrobial effect of the lactic acid bacteria was attributed to acid production. In fermenting cassava dough, enteric pathogens survived to different extents depending on pH and their sensitivity to acids. Vibrio cholerae C-230, Salmonella typhimurium 9 and Salmonella enteritidis 226 were not detectable in 10 g of sample after 4 h when inoculated into the 48-h fermented product, agbelima, whereas Shigella dysenteriae 2357T and Escherichia coli D2188 were detectable up to 8 h in the product.