Substance-centric

Substance-centric refers to an approach, viewpoint, or system that primarily emphasizes, focuses on, or is centered around the material or essential qualities of a specific substance, entity, or field of study. It prioritizes the understanding, analysis, and manipulation of these inherent properties. This contrasts with approaches that prioritize process, form, relationships, or other non-material aspects. In essence, a substance-centric perspective treats the underlying substance as the primary driver of its function, behavior, or impact, often relegating other considerations to a secondary role. It aims to explain phenomena through the inherent properties of a specific element, compound, or concept.

Substance-centric meaning with examples

• The biochemist adopted a substance-centric approach to studying the enzyme, focusing intently on the specific amino acid sequences and their direct impact on catalytic activity. This method allowed her to isolate the crucial elements of the substance without unnecessary distractions. The research concentrated solely on chemical interactions of the enzyme's composition, to the exclusion of external factors or regulatory mechanisms.
• In early pharmaceutical research, the focus was largely substance-centric. Scientists were primarily concerned with isolating and synthesizing novel chemical compounds. They would then observe their direct effects on biological systems, with less attention given to how the drug was delivered or how its interactions would affect the body or metabolism, to derive a substance-focused hypothesis.
• The analysis of a piece of art using a substance-centric framework would primarily scrutinize the physical materials used – the pigments, canvas, and binding agents. The focus will be heavily influenced by the artist's intentions, and by examining the chemical composition and the qualities of the materials used to create the artwork; how those components interact with one another, and how they create the final effect.
• Many modern AI researchers are shifting away from a substance-centric model of computation, that is to define information exclusively by hardware, and instead toward approaches that incorporate the complex architecture of neural networks. In this perspective, the software algorithms and the connections between components are just as important as the processing cores and memory chips.