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A still unexplored class of heterostructured bifunctional catalysts for oxygen evolution (OER) and reduction (ORR) reactions is investigated: Ni−Fe sulfides deposited onto a N,S‐co‐doped zeolite‐templated carbon (ZTC) substrate achieved upon the impregnation of a ZTC with thiourea and Ni and Fe precursors (Ni/Fe atomic ratio of 1). By heat‐treating the impregnated ZTC substrate at 700 °C under N2 atmosphere the efficient bifunctional catalyst is generated. A difference of only 0.76 V is measured between the potential required to drive an OER current density of 10 mA cm−2 and the ORR half‐wave potential. Using electrochemical measurements and physico‐chemical characterizations, it is evidenced that OER activity results from the presence of a sulfide containing both Fe and Ni whereas the ORR activity originates from the N,S‐doped ZTC substrate. The selectivity of the ORR process is improved through the presence of sulfide phases. Compared to more conventional carbon substrate such as N,S‐co‐doped reduced graphene oxide, high specific surface area ZTC favors high ORR performances. The functional ZTC material was further successfully implemented as bifunctional air electrode in a coin‐type Zn‐air battery. [ABSTRACT FROM AUTHOR]

Background: An estimated 6.7 million persons are living with dementia in the United States, a number expected to double by 2060. Persons experiencing moderate to severe dementia are 4 to 5 times more likely to fall than those without dementia, due to agitation and unsteady gait. Socially assistive robots fail to address the changing emotional states associated with agitation, and it is unclear how emotional states change, how they impact agitation and gait over time, and how social robots can best respond by showing empathy., Objective: This study aims to design and validate a foundational model of emotional intelligence for empathetic patient-robot interaction that mitigates agitation among those at the highest risk: persons experiencing moderate to severe dementia., Methods: A design science approach will be adopted to (1) collect and store granular, personal, and chronological data using Personicle (an open-source software platform developed to automatically collect data from phones and other devices), incorporating real-time visual, audio, and physiological sensing technologies in a simulation laboratory and at board and care facilities; (2) develop statistical models to understand and forecast the emotional state, agitation level, and gait pattern of persons experiencing moderate to severe dementia in real time using machine learning and artificial intelligence and Personicle; (3) design and test an empathy-focused conversation model, focused on storytelling; and (4) test and evaluate this model for a care companion robot (CCR) in the community., Results: The study was funded in October 2023. For aim 1, architecture development for Personicle data collection began with a search for existing open-source data in January 2024. A community advisory board was formed and met in December 2023 to provide feedback on the use of CCRs and provide personal stories. Full institutional review board approval was received in March 2024 to place cameras and CCRs at the sites. In March 2024, atomic marker development was begun. For aim 2, after a review of open-source data on patients with dementia, the development of an emotional classifier was begun. Data labeling was started in April 2024 and completed in June 2024 with ongoing validation. Moreover, the team established a baseline multimodal model trained and validated on healthy-person data sets, using transformer architecture in a semisupervised manner, and later retrained on the labeled data set of patients experiencing moderate to severe dementia. In April 2024, empathy alignment of large language models was initiated using prompt engineering and reinforcement learning., Conclusions: This innovative caregiving approach is designed to recognize the signs of agitation and, upon recognition, intervene with empathetic verbal communication. This proposal has the potential to have a significant impact on an emerging field of computational dementia science by reducing unnecessary agitation and falls of persons experiencing moderate to severe dementia, while reducing caregiver burden., International Registered Report Identifier (irrid): PRR1-10.2196/55761., (©Adeline Nyamathi, Nikil Dutt, Jung-Ah Lee, Amir M Rahmani, Mahkameh Rasouli, Donna Krogh, Erik Krogh, David Sultzer, Humayun Rashid, Hamza Liaqat, Riyam Jawad, Farhan Azhar, Ali Ahmad, Bilal Qamar, Taha Yasin Bhatti, Chet Khay, Jocelyn Ludlow, Lisa Gibbs, Julie Rousseau, Mahyar Abbasian, Yutong Song, Cheonkam Jeong, Sabine Brunswicker. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 04.10.2024.)

The in situ confinement of nickel (Ni)-iron (Fe) nanoparticles (NPs) in a polymer-derived microporous silicon carboxynitride (Si-C-O-N)-based support is investigated to stimulate superior oxygen evolution reaction (OER) activity in an alkaline media. Firstly, we consider a commercial polysilazane (PSZ) and Ni and Fe chlorides to be mixed in N,N-dimethylformamide (DMF) and deliver after overnight solvent reflux a series of Ni-Fe : organosilicon coordination polymers. The latter are then heat-treated at 500 °C in flowing argon to form the title compounds. By considering a Ni : Fe ratio of 1.5, face centred cubic (fcc) Ni x Fe y alloy NPs with a size of 15-30 nm are in situ generated in a porous Si-C-O-N-based matrix displaying a specific surface area (SSA) as high as 237 m 2  ⋅ g -1 . Hence, encapsulated NPs are rendered accessible to promote electrocatalytic water oxidation. An OER overpotential as low as 315 mV at 10 mA ⋅ cm -2 is measured. This high catalytic performance (considering that the metal mass loading is as low as 0.24 mg cm -2 ) is rather stable as observed after an activation step; thus, validating our synthesis approach. This is clearly attributed to both the strong NP-matrix interaction and the confinement effect of the matrix as highlighted through post mortem microscopy observations., (© 2024 Wiley-VCH GmbH.)