High-velocity stars and peculiar G objects orbit the central supermassive black hole (SMBH) Sagittarius A* (Sgr A*). Together, the G objects and high-velocity stars constitute the S cluster. In contrast with theoretical predictions, no binary system near Sgr A* has been identified. Here, we report the detection of a spectroscopic binary system in the S cluster with the masses of the components of 2.80 ± 0.50 M and 0.73 ± 0.14 M, assuming an edge-on configuration. Based on periodic changes in the radial velocity, we find an orbital period of 372±3 days for the two components. The binary system is stable against the disruption by Sgr A* due to the semi-major axis of the secondary being 1.59±0.01 AU, which is well below its tidal disruption radius of approximately 42.4 AU. The system, known as D9, shows similarities to the G objects. We estimate an age for D9 of yr that is comparable to the timescale of the SMBH-induced von Zeipel-Lidov-Kozai cycle period of about 10 yr, causing the system to merge in the near future. Consequently, the population of G objects may consist of pre-merger binaries and post-merger products. The detection of D9 implies that binary systems in the S cluster have the potential to reside in the vicinity of the supermassive black hole Sgr A* for approximately 10 years.
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http://dx.doi.org/10.1038/s41467-024-54748-3 | DOI Listing |
OMICS
January 2025
OMICS: A Journal of Integrative Biology, New Rochelle, New York, USA.
UN Women is the United Nations "entity dedicated to gender equality and the empowerment of women". UN Women is an example of the institutions of global governance that followed the gender turn in women's rights over the past 2 decades. This opinion commentary unpacks a brief history of UN Women, and the ongoing disparities in gender diversity, equity, and inclusion (DEI) in science, engineering, and medicine, not to mention in science communication, with the aim to shed light on the adverse impacts of gender essentialism and gender binary.
View Article and Find Full Text PDFRSC Adv
January 2025
Institute of Resources and Environmental Engineering, Shanxi University, Shanxi Yellow River Laboratory Taiyuan China
Coal combustion generates soot-type air pollution, and NO, as a typical pollutant, is the main haze-causing pollutant. The degradation of NO by means of photocatalytic superhydrophobic multifunctional coatings is both durable and economical. The precipitation method was employed to create a p-n type BiOBr/α-FeO photocatalytic binary system.
View Article and Find Full Text PDFThe development of materials from renewable resources has been increasing, intending to reduce the consumption of fossil sources, with terpenes being one of the main families that reduce the consumption of isoprene. The study of the binary catalytic system neodymium versatate/dibutyl magnesium (NdV/Mg(-Bu)), for the coordination homopolymerization of β-myrcene and β-farnesene, was carried out analysing different [Nd] : [Mg] ratios (between 4 and 10). Reporting conversions of 92% and 83% at an [Nd] : [Mg] ratio of 8 for polymyrcene (PMy) and polyfarnesene (PFa), respectively, and microstructures comprising 1,4 content above 80% for both polymers (PMy, -59% and PFa, -83%).
View Article and Find Full Text PDFHeliyon
January 2025
Graduate School of International Agricultural Technology, Department of Green Eco System, Engineering, Seoul National University, Pyeongchang, 25354, Gangwon-do, South Korea.
Organic contaminants from wastewater toxicity to the environment has increased during the last few decades and, therefore, there is an urgent need to decontaminate wastewater prior to disposal. This study aimed to create a high surface area catalytic activated carbon (AC) under same carbonization conditions for phenol and methylene blue (organic wastewater) decontamination. husk (MH), sesame husk (SH), and baobab husk (BH) were used to prepare activated carbon for the removal of methylene blue (MB) and phenol (Ph).
View Article and Find Full Text PDFActas Esp Psiquiatr
January 2025
Department of Clinical Laboratory, Ningbo No.2 Hospital, 315099 Ningbo, Zhejiang, China.
Background: Accurate diagnosis and classification of Alzheimer's disease (AD) are crucial for effective treatment and management. Traditional diagnostic models, largely based on binary classification systems, fail to adequately capture the complexities and variations across different stages and subtypes of AD, limiting their clinical utility.
Methods: We developed a deep learning model integrating a dot-product attention mechanism and an innovative labeling system to enhance the diagnosis and classification of AD subtypes and severity levels.
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