Design Of An Optofluidic Device For The Measurement Of The Elastic Modulus Of Deformable Particles

Suspensions carrying deformable inclusions are ubiquitous in nature and applications. Hence, high-throughput characterization of the mechanical properties of soft particles is of great interest. Recently, a non-invasive optofluidic technique has been developed for the measurement of the interfacial tension between two immiscible liquids [8]. We have adapted such technique to the case of soft solid beads, thus designing a non-invasive optofluidic device for the measurement of the mechanical properties of deformable particles from real-time optical imaging of their deformation. The device consists of a cylindrical microfluidic channel with a cross-section reduction in which we make initially spherical soft beads flow suspended in a Newtonian carrier. By imaging the deformation of a particle in real time while it goes through the constriction, it is possible to get a measure of its elastic modulus through a theoretically derived-correlation. We provide both experimental and numerical validation of our device

Correction: Rheometry-on-a-chip: measuring the relaxation time of a viscoelastic liquid through particle migration in microchannel flows

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L’appropriatezza nella gestione dell’iperglicemia nel paziente ospedalizzato: schemi di orientamento

Clinical governance e appropriatezza nel management dell’iperglicemia in ospedale I numeri del diabete  Nel mondo   In Italia Il diabete nella real life ospedaliera L’appropriatezza nella gestione dell’iperglicemia nella fase del ricovero in ospedale  L’appropriatezza nella definizione della terapia insulinica e degli obiettivi glicemici     Perché sospendere gli ipoglicemizzanti orali?      Distinzione fra paziente critico e acuto non critico      Paziente critico      Paziente acuto non critico      Schema basal bolus      Schema basal plus      Transizione dalla somministrazione endovena alla somministrazione sottocute      Perché no sliding scale?      Obiettivi glicemici      L’appropriatezza nella somministrazione dell’insulina sottocute     Vantaggi in termini di costi      Considerazioni riassuntive sull’impiego del sistema siringa/flacone     Considerazioni riassuntive sull’impiego degli iniettori a penna     Aree di miglioramento     Appropriatezza del trattamento con le penne pre-riempite in ospedale     L’appropriatezza nella gestione dell’iperglicemia nel paziente in terapia cortisonica     Come comportarsi?      L’appropriatezza nella gestione dell’iperglicemia nel paziente candidato al trattamento chirurgico     Cosa deve fare l’internista?     Dopo l’intervento chirurgico ed il rientro in reparto     L’appropriatezza nella rilevazione e nel trattamento delle ipoglicemie      Definizione     Prevalenza nelle Unità Operative di Medicina Interna     Fattori di rischio     Esiti      Prevenzione      Trattamento     L’appropriatezza nella gestione dell’iperglicemia nella fase della dimissione     Conclusioni     Appendix     Bibliografia 

Self-driving Multimodal Studies at User Facilities

Multimodal characterization is commonly required for understanding materials. User facilities possess the infrastructure to perform these measurements, albeit in serial over days to months. In this paper, we describe a unified multimodal measurement of a single sample library at distant instruments, driven by a concert of distributed agents that use analysis from each modality to inform the direction of the other in real time. Powered by the Bluesky project at the National Synchrotron Light Source II, this experiment is a world's first for beamline science, and provides a blueprint for future approaches to multimodal and multifidelity experiments at user facilities.Comment: 36th Conference on Neural Information Processing Systems (NeurIPS 2022). AI4Mat Worksho

Crystallography companion agent for high-throughput materials discovery

The discovery of new structural and functional materials is driven by phase identification, often using X-ray diffraction (XRD). Automation has accelerated the rate of XRD measurements, greatly outpacing XRD analysis techniques that remain manual, time-consuming, error-prone, and impossible to scale. With the advent of autonomous robotic scientists or self-driving labs, contemporary techniques prohibit the integration of XRD. Here, we describe a computer program for the autonomous characterization of XRD data, driven by artificial intelligence (AI), for the discovery of new materials. Starting from structural databases, we train an ensemble model using a physically accurate synthetic dataset, which output probabilistic classifications -- rather than absolutes -- to overcome the overconfidence in traditional neural networks. This AI agent behaves as a companion to the researcher, improving accuracy and offering significant time savings. It was demonstrated on a diverse set of organic and inorganic materials characterization challenges. This innovation is directly applicable to inverse design approaches, robotic discovery systems, and can be immediately considered for other forms of characterization such as spectroscopy and the pair distribution function.Comment: For associated code, see https://github.com/maffettone/xc

What is missing in autonomous discovery: Open challenges for the community

Self-driving labs (SDLs) leverage combinations of artificial intelligence, automation, and advanced computing to accelerate scientific discovery. The promise of this field has given rise to a rich community of passionate scientists, engineers, and social scientists, as evidenced by the development of the Acceleration Consortium and recent Accelerate Conference. Despite its strengths, this rapidly developing field presents numerous opportunities for growth, challenges to overcome, and potential risks of which to remain aware. This community perspective builds on a discourse instantiated during the first Accelerate Conference, and looks to the future of self-driving labs with a tempered optimism. Incorporating input from academia, government, and industry, we briefly describe the current status of self-driving labs, then turn our attention to barriers, opportunities, and a vision for what is possible. Our field is delivering solutions in technology and infrastructure, artificial intelligence and knowledge generation, and education and workforce development. In the spirit of community, we intend for this work to foster discussion and drive best practices as our field grows