Physicochemical properties of a mineral-based gasoline engine oil have been monitored at 0, 500, 1000, 2000, 3500, 6000, 8500, and 11500 kilometer of operation. Tracing has been performed by inductively coupled plasma and some other techniques. At each series of measurements, the concentrations of twenty four elements as well as physical properties such as: viscosity at 40 and 100°C; viscosity index; flash point; pour point; specific gravity; color; total acid and base numbers; water content have been determined. The results are indicative of the decreasing trend in concentration of additive elements and increasing in concentration for wear elements. Different trends have been observed for various physical properties. The possible reasons for variations in physical and chemical properties have been discussed.
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http://dx.doi.org/10.1155/2012/819524 | DOI Listing |
J Dent Sci
January 2025
School of Dental Technology, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan.
Background/purpose: Early osseointegration of titanium (Ti) dental implants relies on the surface topography. Surface modification of Ti seeks to enhance bone regeneration around implants. Acid etching is the simple, less technique sensitive and cost-effective technique for surface treatment.
View Article and Find Full Text PDFComput Struct Biotechnol J
December 2024
NovaMechanics Ltd, Nicosia 1070, Cyprus.
The CompSafeNano project, a Research and Innovation Staff Exchange (RISE) project funded under the European Union's Horizon 2020 program, aims to advance the safety and innovation potential of nanomaterials (NMs) by integrating cutting-edge nanoinformatics, computational modelling, and predictive toxicology to enable design of safer NMs at the earliest stage of materials development. The project leverages Safe-by-Design (SbD) principles to ensure the development of inherently safer NMs, enhancing both regulatory compliance and international collaboration. By building on established nanoinformatics frameworks, such as those developed in the H2020-funded projects NanoSolveIT and NanoCommons, CompSafeNano addresses critical challenges in nanosafety through development and integration of innovative methodologies, including advanced models, approaches including machine learning (ML) and artificial intelligence (AI)-driven predictive models and 1st-principles computational modelling of NMs properties, interactions and effects on living systems.
View Article and Find Full Text PDFIn Silico Pharmacol
January 2025
Department of Biotechnology and Bioinformatics, JSS Academy of Higher Education and Research, Mysore, Karnataka 570015 India.
Unlabelled: Parkinson's Disease (PD) is a neurodegenerative disorder that primarily affects persons aged 65 and older. It leads to a decline in motor function as a result of the buildup of abnormal protein deposits called Lewy bodies in the brain. Existing therapies exhibit restricted effectiveness and undesirable side effects.
View Article and Find Full Text PDFAdv Mater
January 2025
Key Laboratory of Organic Optoelectronics and Molecular Engineering of the Ministry of Education, Department of Chemistry, Tsinghua University, Beijing, 100084, P. R. China.
Advanced carbon materials are widely utilized in wearable electronics. Nevertheless, the production of carbon materials from fossil-based sources raised concerns regarding their non-renewability, high energy consumption, and the consequent greenhouse gas emissions. Biopolymers, readily available in nature, offer a promising and eco-friendly alternative as a carbon source, enabling the sustainable production of carbon materials for wearable electronics.
View Article and Find Full Text PDFSmall
January 2025
College of Physical Science and Technology, Xiamen University, Xiamen, 361005, P. R. China.
Twisted bilayer graphene (TBG) has drawn considerable attention due to its angle-dependent electrical, optical, and mechanical properties, yet preparing and identifying samples at specific angles on a large scale remains challenging and labor-intensive. Here, a data-driven strategy that leverages Raman spectroscopy is proposed in combination with deep learning to rapidly and non-destructively decode and predict the twist angle of TBG across the full angular range. By processing high-dimensional Raman data, the deep learning model extracts hidden information to achieve precise twist angle identification.
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