Technological Lock-in and the Democratisation of Environmental Data: A Comparative Scientometric Analysis of Gas and Liquid Sensing Trajectories

Digital innovation has democratised data processing. However, the physical acquisition of environmental data remains stratified, excluding non-experts and developing regions from water quality stewardship. The Internet of Things (IoT) and Artificial Intelligence (AI) have shifted the economic value of sensing from precision to data density. While gas sensing has successfully transitioned into a ubiquitous, software-defined commodity, liquid-phase sensing, especially Ion-Selective Electrodes (ISEs), remain locked in a high-cost, low-volume niche. This study presents a comparative longitudinal analysis of the economic, technological, and bibliometric trajectories of both fields to elucidate the drivers of this divergence. Using term co-occurrence networks from 1.3 million gas sensing publications and 109,000 ISE publications (1980–2024), we reveal a strong semantic migration in the gas sector from device physics to application-centric domains, contrasting with the ISE field's persistent fixation on material formulation. A "commoditisation feedback loop" was identified in the gas market, triggered by the expiration of key patents and the collapse of unit costs below $2.00, which unlocked a long tail of open-source innovation and data generation. In contrast, the ISE market remains stifled by the need of a Reference Electrode, and the high capital cost of legacy glass architectures, limiting annual research output to a fraction of its gas counterpart. We argue that the barriers to liquid sensing ubiquity are not intrinsic performance flaws, such as drift or selectivity, but rather the prohibitive marginal cost of experimentation. We conclude that to replicate the gas sensor revolution, the liquid sensing community must pivot from the pursuit of "perfect" chemical specificity to "sufficient" digital utility, leveraging emerging scalable manufacturing techniques and AI-driven sensor arrays to democratise hydrological data acquisition.