Secondary organic aerosols (SOA) are a major contributor to fine particulate mass and wield substantial influences on the Earth's climate and human health. Despite extensive research in recent years, many of the fundamental processes of SOA formation and evolution remain poorly understood. Most atmospheric aerosol models use gas/particle equilibrium partitioning theory as a default treatment of gas-aerosol transfer, despite questions about potentially large kinetic effects. We have conducted fundamental SOA formation experiments in a Teflon environmental chamber using a novel method. A simple chemical system produces a very fast burst of low-volatility gas-phase products, which are competitively taken up by liquid organic seed particles and Teflon chamber walls. Clear changes in the species time evolution with differing amounts of seed allow us to quantify the particle uptake processes. We reproduce gas- and aerosol-phase observations using a kinetic box model, from which we quantify the aerosol mass accommodation coefficient (α) as 0.7 on average, with values near unity especially for low volatility species. α appears to decrease as volatility increases. α has historically been a very difficult parameter to measure with reported values varying over 3 orders of magnitude. We use the experimentally constrained model to evaluate the correction factor (Φ) needed for chamber SOA mass yields due to losses of vapors to walls as a function of species volatility and particle condensational sink. Φ ranges from 1-4.
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http://dx.doi.org/10.1021/acs.est.7b02144 | DOI Listing |
Surg Technol Int
January 2025
JIS Orthopedics Inc., New Albany, Ohio.
Accurate acetabular component positioning is crucial for the success of total hip arthroplasty (THA). Malplacement of the acetabular component increases the risk of post-surgery complications, most notably dislocation.1 Furthermore, malposition can also result in wear of the polyethylene liner, limited range of motion, and osteolysis.
View Article and Find Full Text PDFThe inertial element of a solid block is commonly used as the proof mass in traditional accelerometers. However, it is challenging to accommodate both the high-density solid-state proof mass and the highly elastic component simultaneously in a miniature sensor, which makes it difficult for the sensors to maintain comparable sensing performance at a miniaturized size. Here, a novel, to the best of our knowledge, liquid metal-based fiber optic accelerometer (LMFOA) is proposed for the first time to meet this requirement.
View Article and Find Full Text PDFEBioMedicine
January 2025
MGH Biostatistics Center, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA. Electronic address:
Background: The ovarian cancer (OC) preclinical detectable phase (PCDP), defined as the interval during which cancer is detectable prior to clinical diagnosis, remains poorly characterised. We report exploratory analyses from the United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS).
Methods: In UKCTOCS between Apr-2001 and Sep-2005, 101,314 postmenopausal women were randomised to no screening (NS) and 50,625 to annual multimodal screening (MMS) (until Dec-2011) using serum CA-125 interpreted by the Risk of Ovarian Cancer Algorithm (ROCA).
J Sleep Res
January 2025
School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia.
Australia's mine sites are largely situated in remote locations and operate around the clock. Many shift workers fly to site, where they work 12-hr shifts and sleep in camp accommodation before they return home for the period rostered off work. Mining shift workers experience poor sleep, yet limited research is available on contributing factors.
View Article and Find Full Text PDFNat Med
January 2025
Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA.
Large language models (LLMs) are rapidly being adopted in healthcare, necessitating standardized reporting guidelines. We present transparent reporting of a multivariable model for individual prognosis or diagnosis (TRIPOD)-LLM, an extension of the TRIPOD + artificial intelligence statement, addressing the unique challenges of LLMs in biomedical applications. TRIPOD-LLM provides a comprehensive checklist of 19 main items and 50 subitems, covering key aspects from title to discussion.
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