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Microreactors have gained widespread attention from academia and industrial researchers due to their exceptionally fast mass and heat transfer and flexible control. In this work, CiteSpace software was used to systematically analyze the relevant literature to gain a comprehensively understand on the research status of microreactors in various field...
Citations
Purpose
Nanomedicine has significant potential to revolutionize biomedicine and healthcare through innovations in diagnostics, therapeutics, and regenerative medicine. This study aims to develop a novel framework that integrates advanced natural language processing, noise-free topic modeling, and multidimensional bibliometrics to systematically identify emerging nanomedicine technology topics from scientific literature.
Design/methodology/approach
The framework involves collecting full-text articles from PubMed Central and nanomedicine-related metrics from the Web of Science for the period 2013–2023. A fine-tuned BERT model is employed to extract key informative sentences. Noiseless Latent Dirichlet Allocation (NLDA) is applied to model interpretable topics from the cleaned corpus. Additionally, we develop and apply metrics for novelty, innovation, growth, impact, and intensity to quantify the emergence of novel technological topics.
Findings
By applying this methodology to nanomedical publications, we identify an increasing emphasis on research aligned with global health priorities, particularly inflammation and biomaterial interactions in disease research. This methodology provides deeper insights through full-text analysis and leading to a more robust discovery of emerging technologies.
Research limitations
One limitation of this study is its reliance on the existing scientific literature, which may introduce publication biases and language constraints. Additionally, manual annotation of the dataset, while thorough, is subject to subjectivity and can be time-consuming. Future research could address these limitations by incorporating more diverse data sources, and automating the annotation process.
Practical implications
The methodology presented can be adapted to explore emerging technologies in other scientific domains. It allows for tailored assessment criteria based on specific contexts and objectives, enabling more precise analysis and decision-making in various fields.
Originality/value
This study offers a comprehensive framework for identifying emerging technologies in nanomedicine, combining theoretical insights and practical applications. Its potential for adaptation across scientific disciplines enhances its value for future research and decision-making in technology discovery.
Since the concept of microreactor was first proposed in the 1980s, it has been entering the chemistry and chemical engineering fields at a high speed and is favored by both scientific and commercial fields. The development of microreactors has been extensively studied in the literature, but less analysis has been carried out from the perspective of industrial technology. This paper aims to identify, analyze, and map the technological profile of microreactors from the perspective of patents. Based on the text mining techniques, a quantitative analysis of 16,327 microreactor‐related patents in the Derwent Innovations Index database was conducted. The analysis reveals a consistent annual increase in the number of microreactor‐related patents from 1982 to 2021. Specifically, China emerges as the leading country in terms of patent disclosures, whereas the United States and the World Intellectual Property Organization (WIPO) play a crucial role in knowledge transfer. Moreover, this study identifies the technological evolution path for microreactors and analyzes in detail the key patents along this evolution path. Individuation, modularization, and greening are the developing directions of microreactor technology in the future. This research offers valuable guidance for researchers and industry professionals in decision‐making and driving advancements in microreactor technology.
Artificial intelligence (AI) offers transformative potential for chemical research through its ability to optimize reactions and processes, enhance energy efficiency, and reduce waste. AI-assisted chemical research (AI + chem) has become a global hotspot. To better understand the current research status of "AI + chem", this study conducted a scientific bibliometric investigation using CiteSpace. The web of science core collection was utilized to retrieve original articles related to "AI + chem" published from 2000 to 2024. The obtained data allowed for the visualization of the knowledge background, current research status, and latest knowledge structure of "AI + chem". The "AI + chem" has entered a stage of explosive growth, and the number of papers will maintain long-term high-speed growth. This article systematically analyzes the latest progress in "AI + chem" and objectively predicts future trends, including molecular design, reaction prediction, materials design, drug design, and quantum chemistry. The outcomes of this study will provide readers with a comprehensive understanding of the overall landscape of "AI + chem".
This work presents a novel microfluidic screening setup with real-time analytics for investigating reactions with immobilised biocatalysts. The setup combines microreactor technology, multi-reactor integration, and online (chip-)LC/MS analysis in a...
Although Europe is the continent with the highest proportion of karst areas, where hydrological systems are essential but extremely sensitive, data on the ecological status of karst riverine catchments are scarce. The aim of the present study was to assess the spatial and temporal (long-term and seasonal) variability of the physico-chemical and organic water parameters in the headwaters of the Krka River and its tributaries, as representatives of a typical karst ecosystem, situated in one of the largest karst areas in Europe, Dinarides in Croatia. It is affected in its upper reaches by improperly treated wastewaters, so anthropogenic influences and ecological status were estimated with the aim to present consequences of pollution exposure and importance of strict monitoring of such sensitive karst ecosystems worldwide. Results indicated degraded water quality, poor ecological status, and disturbed seasonal fluctuations at wastewater-influenced sites, primarily due to high levels of nutrients and organic matter. However, improvement was observed downstream in the Krka National Park, confirming the self-purification as important processes in dynamic karst rivers. Natural seasonality, observed at sites without wastewater influence, was mainly driven by fluctuations in water levels and primary production during the year. Literature analysis by CiteSpace pointed to scarce data on this topic worldwide (China and the USA account for 49% of all publications) and in Europe (34%). Therefore, such study is a valuable contribution in presenting the long-term and seasonal variability of ecological water parameters and in providing a more comprehensive understanding of the health of catchment under influence of multiple stressors.
Flow chemistry has unlocked a world of possibilities for the synthetic community, but the idea that it is a mysterious "black box" needs to go. In this review, we show that several of the benefits of microreactor technology can be exploited to push the boundaries in organic synthesis and to unleash unique reactivity and selectivity. By "lifting the veil" on some of the governing principles behind the observed trends, we hope that this review will serve as a useful field guide for those interested in diving into flow chemistry.
