Photoredox catalysis is becoming more and more prevalent in the 21st century as a new tool for organic and polymer synthesis. In addition, this domain clearly fits the expectations of the twelve principles of green chemistry. However, access to metal containing photosensitizers is not always straightforward and can require long reaction times, the use of toxic solvents and multi-step synthesis. These are definitely drawbacks that could be overcome with the use of novel technologies. In this report, we develop a one-pot two-step synthesis of iron(II) photosensitizers using ball-milling. Overall reaction times were drastically reduced, no solvent was needed during the reaction, and ten complexes could be isolated in high yields (73–99%). Using a transparent milling jar, the formation of the complexes could be followed using in situ Raman spectroscopy.

Importance : Pain is a common out-of-hospital symptom among patients, and opioids are often prescribed. Research suggests that overprescribing for acute traumatic pain is still prevalent, even when limits restricting opioid prescriptions have been implemented. Ketamine hydrochloride is an alternative to opioids in adults with out-of-hospital traumatic pain. Objective : To assess the noninferiority of intravenous ketamine compared with intravenous morphine sulfate to provide pain relief in adults with out-of-hospital traumatic pain. Design, Setting, and Participants : The Intravenous Subdissociative-Dose Ketamine Versus Morphine for Prehospital Analgesia (KETAMORPH) study was a multicenter, single-blind, noninferiority randomized clinical trial comparing ketamine hydrochloride (20 mg, followed by 10 mg every 5 minutes) with morphine sulfate (2 or 3 mg every 5 minutes) in adult patients with out-of-hospital trauma and a verbal pain score equal to or greater than 5. Enrollment occurred from November 23, 2017, to November 26, 2022, in 11 French out-of-hospital emergency medical units. Interventions : Patients were randomly assigned to ketamine (n = 128) or morphine (n = 123). Main Outcomes and Measures : The primary outcome was the between-group difference in mean change in verbal rating scale pain scores measured from the time before administration of the study drug to 30 minutes later. A noninferiority margin of 1.3 was chosen. Results : A total of 251 patients were randomized (median age, 51 [IQR, 34-69] years; 111 women [44.9%] and 140 men [55.1%] among the 247 with data available) and were included in the intention-to-treat population. The mean pain score change was −3.7 (95% CI, −4.2 to −3.2) in the ketamine group compared with −3.8 (95% CI, −4.2 to −3.4) in the morphine group. The difference in mean pain score change was 0.1 (95% CI, −0.7 to 0.9) points. There were no clinically meaningful differences for vital signs between the 2 groups. The intravenous morphine group had 19 of 113 (16.8% [95% CI, 10.4%-25.0%]) adverse effects reported (most commonly nausea [12 of 113 (10.6%)]) compared with 49 of 120 (40.8% [95% CI, 32.0%-49.6%]) in the ketamine group (most commonly emergence phenomenon [24 of 120 (20.0%)]). No adverse events required intervention. Conclusions and Relevance : In the KETAMORPH study of patients with out-of-hospital traumatic pain, the use of intravenous ketamine compared with morphine showed noninferiority for pain reduction. In the ongoing opioid crisis, ketamine administered alone is an alternative to opioids in adults with out-of-hospital traumatic pain. Trial Registration : ClinicalTrials.gov Identifier: NCT03236805

We develop a machine learning model, which predicts structural relaxation from amorphous supercooled liquid structures. The trained networks are able to predict dynamic heterogeneity across a broad range of temperatures and time scales with excellent accuracy and transferability. We use the network transferability to predict dynamic heterogeneity down to the experimental glass transition temperature Tg, where structural relaxation cannot be analyzed using molecular dynamics simulations. The results indicate that the strength, the geometry, and the characteristic length scale of the dynamic heterogeneity evolve much more slowly near Tg compared to their evolution at higher temperatures. Our results show that machine learning techniques can provide physical insights on the nature of the glass transition that cannot be gained using conventional simulation techniques.