Au-decorated TiCT/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 TiCT-MXene (MX) and gold nanoparticles (Au NPs) constructs a versatile immunosensing platform for detecting extracellular matrix protein-1 (ECM1), a breast cancer-associated biomarker.

Investigation of enhanced H 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 H production. The experiments were conducted using a horizontal tube reactor under different parameters: temperatures, MSW loadings, residence times, and equivalence ratios.

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.