Investigation of enhanced H2 production from municipal solid waste gasification via artificial neural network with data on tar compounds.

An artificial neural network (ANN) is a biologically inspired computational technique that imitates the behavior and learning process of the human brain. In this study, ANN technique was applied to assess the gasification of municipal solid waste (MSW) with the aim of enhancing the H2 production. The experiments were conducted using a horizontal tube reactor under different parameters: temperatures, MSW loadings, residence times, and equivalence ratios. The input and output variables (released gases) were tested and trained using back-propagation algorithm, and the data distribution by K-fold contrivance. The values of the training (80% data) and validation (20% data) dataset were found satisfactory. The values of regression coefficient (R2) for the training phase were lied between 0.9392 and 0.9991, and 0.9363 and 0.993824 for the testing phase. Whereas; the values of root mean square error (RSME) for the training phase were lied between 0.4111 and 0.8422, and between 0.1476 and 0.7320 for the testing phase. Higher H2 production of 42.1 vol% was produced at the higher reaction temperature of 900 °C with LHV of 11.2 MJ/Nm3. According to the tar analysis, the dominant compounds were aromatics (17 compounds) followed by polycyclic aromatic, phenyl, aliphatic, aromatic heterocyclic, polycyclic, and aromatic ketone compounds.

Au-decorated Ti3C2Tx/porous carbon immunoplatform for ECM1 breast cancer biomarker detection with machine learning computation for predictive accuracy.

Electrochemical immunosensors, surpassing conventional diagnostics, exhibit significant potential for cancer biomarker detection. However, achieving a delicate balance between signal sensitivity and operational stability, especially at the heterostructure interface, is crucial for practical immunosensors. Herein, porous carbon (PC) integration with Ti3C2Tx-MXene (MX) and gold nanoparticles (Au NPs) constructs a versatile immunosensing platform for detecting extracellular matrix protein-1 (ECM1), a breast cancer-associated biomarker. The inclusion of PC provided robust structural support, enhancing electrolytic diffusion with an expansive surface area while synergistically facilitating charge transfer with Ti3C2Tx. The biosensor optimized with 1.0 mg PC demonstrates a robust electrochemical redox response to the surface-bound thionine (th) redox probe, utilizing an inhibition-based strategy for ECM1 detection. The robust antibody-antigen interactions across the PC-integrated Ti3C2Tx-Au NPs platform (MX-Au-C-1) enabled robust ECM1 detection within 0.1-7.5 nM, with a low limit of detection (LOD) of 0.012 nM. The constructed biosensor shows improved operational stability with a 98.6 % current retention over 1 h, surpassing MXene-integrated (MX-Au) and pristine Au NPs (63.2 % and 44.3 %, respectively) electrodes. Moreover, the successful adaptation of the artificial neural network (ANN) model for predictive analysis of the generated DPV data further validates the accuracy of the biosensor, promising its future application in AI-powered remote health monitoring.

Use of waste recycling coal bottom ash and sugarcane bagasse ash as cement and sand replacement material to produce sustainable concrete.

Concrete is widely used as a building material all over the world, and its use is increasing the demand of cement and sand in the construction industry. However, the limited resources and environmental degradation are driving scientists to develop alternative materials from vast volumes of agro-industrial wastes as a partial replacement for conventional cement. In the manufacture of concrete, cement is a major binding resource. This study looked into recycling agro-industrial wastes into cement, such as sugarcane bagasse ash (SCBA), coal bottom ash (CBA), and others, to create sustainable and environmentally friendly concrete. This study aims to see how the combined effects of agricultural by-product wastes affected the characteristics of concrete. SCBA is used to replace fine aggregate (FA) ranging from 0 to 40% by weight of FA, while CBA is used to replace cement content ranging from 0 to 20% by weight of the total binder. In this case, a total of 204 concrete samples (cubes and cylinders) are made using a mixed proportion of 1:1.5:3 and a water-cement ratio of 0.54. Workability, density, water absorption, and mechanical characteristics in terms of compressive and splitting tensile strengths were examined in this study. In addition, for each mix percentage, the total embodied carbon was determined. Workability, density, and water absorption were found to be considerably decreased when CBA and SCBA concentration increased. Due to the pozzolanic nature of CBA and SCBA, an increase in compressive and splitting tensile strengths were seen for specific concrete mixtures, and further addition of CBA and SCBA, the decrease in strength. The embodied carbon of SCBA has augmented the total embodied carbon of concrete, though it can be seen that C15S40, which comprises of 15% CBA and 40% SCBA, is the optimum mix that attained tensile and compressive strength by 3.05 MPa and 28.75 MPa correspondingly, with a 4% reduction in total embodied carbon.