Enhanced bioconversion of dairy and chicken manure by the interaction of exogenous bacteria and black soldier fly larvae.

Generation of insects' biomass from lignocellulose rich organic wastes is of significant challenges in reducing the environmental impact of wastes and in sustaining feed and food security. This research looked at the effects of lignocellulotic exogenous bacteria in the black soldier fly (BSF) organic waste conversion system for biomass production and lignocellulose biodegradation of dairy and chicken manures. Six exogenous bacteria were investigated for cellulolytic activity with carboxymethyl cellulose and found that these tested bacterial strains degrade the cellulose. In this study; a co-conversion process using Hermetia illucens larvae to convert the previously studied best mixing ratio of dairy manure (DM) and chicken manure (CHM) (2:3) and cellulose degrading bacteria was established to enhance the larval biomass production, waste reduction and manure nutrient degradation. BSF larvae assisted by MRO2 (R5) has the best outcome measures: survival rate (99.1%), development time (19.0 d), manure reduction rate (48.7%), bioconversion rate (10.8%), food conversion ratio (4.5), efficiency of conversion of ingestion (22.3), cellulose (72.9%), hemicellulose (68.5%), lignin (32.8%), and nutrient utilization (protein, 71.2% and fat, 67.8%). By analyzing the fiber structural changes by scanning electron microscopy and Fourier-transformed infrared spectroscopy (FT-IR), we assume that exogenous bacteria assist the BSF larvae that trigger lead to structural and chemical modification of fibers. We hypothesized that these surface and textural changes are beneficial to the associated gut bacteria, thereby helping to larval growth and reduce waste. The finding of the investigation showed that enhanced conversion of DM and CHM by BSF larvae assisted with lignocellulotic exogenous bacteria could play key role in the manure management.

Corrigendum: Additive manufacturing of Al2O3 ceramics with MgO/SiC contents by laser powder bed fusion process.

[This corrects the article DOI: 10.3389/fchem.2023.1034473.].

Additive manufacturing of Al2O3 ceramics with MgO/SiC contents by laser powder bed fusion process.

Laser powder bed fusion is a laser-based additive manufacturing technique that uses a high-energy laser beam to interact directly with powder feedstock. LPBF of oxide ceramics is highly desirable for aerospace, biomedical and high-tech industries. However, the LPBF of ceramics remains a challenging area to address. In this work, a new slurry-based approach for LPBF of ceramic was studied, which has some significant advantages compared to indirect selective laser sintering of ceramic powders. LPBF of Al2O3 was fabricated at different MgO loads up to 80 wt%. Several specimens on different laser powers (70 W-120 W) were printed. The addition of magnesia influenced the microstructure of the alumina ceramic significantly. The findings show that when the laser power is high and the magnesia load is low, the surface quality of the printing parts improves. It is feasible to produce slurry ceramic parts without binders through LPBF. Furthermore, the effects of SiC and MgO loads on the microstructure and surface morphology of alumina are compared and analysed.

Printability for additive manufacturing with machine learning: Hybrid intelligent Gaussian process surrogate-based neural network model for Co-Cr alloy.

AM has revolutionized the manufacturing industry, involving several operating parameters that may affect the properties of the final manufactured part. In AM, LPBF has proved its reliability in producing dense components; however, process development for every material necessitates extensive testing. Even the tiniest change can negate all the data for the same material. It is vital to have a P-P correlation that can train itself following a change in powder or machine to achieve defects-free parts and optimal properties. These goals cannot be met alone by multi-physics. One of the ways to address this issue is to apply ML, but it requires a huge data set for training and testing purposes. A framework has been developed for Co-Cr S-S curves to resolve this issue. Twenty-two experimental S-S curves have been generated to produce YS, TS, and EL data points. In combination with DNN, these data points have been applied to the validated and tested GPS-surrogate model to develop a smart processing window to achieve desired YS, TS, and EL. LP, LSS, HD, and PLT have been selected during the whole framework as inputs, while YS, TS, and EL have been classified as outputs. The output of the smart window was verified experimentally. It is found that the highest YS (1110.91 MPa) is attained using LP = 180 W, LSS = 600 mm/s and HD = 70 µm, while least YS (645.05 MPa) is identified using LP = 160 W, LSS = 900 mm/s and HD = 70 µm. For TS, the maximum (165.91 MPa) and minimum (689.73 MPa) values have been achieved using LP = 180 W, LSS = 900 mm/s and HD = 70 µm, and LP = 180 W, LSS = 1000 mm/s and HD = 70 µm, respectively. In the case of EL, LP = 180 W, LSS = 700 mm/s and HD = 70 µm, and LP = 180 W, LSS = 600 mm/s and HD = 70 µm, resulted 23.04% and 0.789% EL, respectively. Using CC, LP and HD did not significantly affect the TS, YS, and EL, while a negative relationship has been found for LSS with TS, YS, and EL. The smart processing window showed that the YS and TS could be achieved at low-high LP and low LSS at the cost of EL. This study provides a technique for framework development in the case of P-P relation based on the provided inputs and the corresponding outputs, leading toward process smartification.