Ionic liquid catalyzed low-temperature hydrothermal carbonization of sewage sludge to produce hydrochar with low heavy metal content and positive energy recovery.

An ionic liquid (IL, [DMAPA]HSO4) was prepared to facilitate the removal of heavy metals by hydrothermal carbonization (HTC) in sewage sludge (SS) and to obtain a positive energy recovery (ER, (Energyoutput/Energyinput - 1) > 0). The results found that the removal efficiencies of the Fe, Mn, Zn, Co, and Cd from SS exceeded 75 % with positive ER (6 %) at 20 wt% IL dosage (IL:SS). IL promoted the HTC reactions of proteins and polysaccharides to produce fixed carbon and small molecule polymers. The process mainly relies on IL to catalyze the dehydration and graphitization of SS and to destroy the heavy metal binding sites such as carboxyl and hydroxyl groups. Additionally, IL aids in constructing the macropore structures in hydrochar, thereby facilitating the release of heavy metals and water during the HTC process. This discovery holds promise for removing heavy metals from SS by one-pot HTC processes with positive energy recovery.

How do providers of artificial intelligence (AI) solutions propose and legitimize the values of their solutions for supporting diagnostic radiology workflow? A technography study in 2021.

OBJECTIVES: How do providers of artificial intelligence (AI) solutions propose and legitimize the values of their solutions for supporting diagnostic radiology workflow? METHODS: We systematically analyze 393 AI applications developed for supporting diagnostic radiology workflow. We collected qualitative and quantitative data by analyzing around 1250 pages of documents retrieved from companies' websites and legal documents. Five investigators read and interpreted collected data, extracted the features and functionalities of the AI applications, and finally entered them into an excel file for identifying the patterns. RESULTS: Over the last 2 years, we see an increase in the number of AI applications (43%) and number of companies offering them (34%), as well as their average age (45%). Companies claim various value propositions related to increasing the "efficiency" of radiology work (18%)-e.g., via reducing the time and cost of performing tasks and reducing the work pressure-and "quality" of offering medical services (31%)-e.g., via enhancing the quality of clinical decisions and enhancing the quality of patient care, or both of them (28%). To legitimize and support their value propositions, the companies use multiple strategies simultaneously, particularly by seeking legal approvals (72%), promoting their partnership with medical and academic institutions (75%), highlighting the expertise of their teams (56%), and showcasing examples of implementing their solutions in practice (53%). CONCLUSIONS: Although providers of AI applications claim a wide range of value propositions, they often provide limited evidence to show how their solutions deliver such systematic values in clinical practice. KEY POINTS: • AI applications in radiology continue to grow in number and diversity. • Companies offering AI applications claim various value propositions and use multiple ways to legitimize these propositions. • Systematic scientific evidence showing the actual effectiveness of AI applications in clinical context is limited.