UFA (United States)

For the last century, gasoline has been the primary fuel source for internal combustion engines in many countries around the world. More recently, the petroleum derived fuel has increasingly been mixed with combustible oxygenates, commonly ethanol. In some countries ethanol has even become the primary or exclusive component of vehicle fuel. This switch has been done for economic and environmental reasons but can decrease engine fuel economy. The experiment detailed here allows students to use bomb calorimetry to create a calibration curve between internal energy of combustion and ethanol content of various fuel blends. The calibration curve can then be used to estimate the ethanol content of commercial gasoline samples. The resulting data and analysis from this experiment gives students the opportunity to explore and discuss what the effects of adding an oxygenate to gasoline are. These effects are considered from the position of changes in internal energy of combustion and how that can be understood in the context of the operating principles of an internal combustion engine.

ABSTRACT We developed a machine learning approach to estimate an explosion yield and height-of-burst or depth-of-burial (HOB or DOB) using a combination of seismic ground-motion and acoustic measurements. The technique employs artificial neural networks (ANNs) with Bayesian regularization suitable for small datasets to reduce the potential for overfitting and improve the network generalization. Using data from multiple explosion experiments conducted in various rock types, we investigated the effect of different seismic and acoustic measurement methods combined with estimated seismic velocity on the yield and the scaled HOB or DOB estimates. The training dataset for the ANN comprised data from 42 explosions conducted in various media at different HOB or DOB. Two additional explosions were used for the method validation. The input features included seismic and acoustic amplitudes, peak frequency, positive phase duration, and the apparent seismic velocity. The presented approach is not limited to a single lithology. Instead, it uses a diverse set of parameters, such as apparent seismic velocity and P-wave peak frequency, to implicitly identify the medium properties within the data-driven framework. Incorporating these parameters resulted in significant improvement of the method performance for both scaled depth and yield estimates. The root mean square error of the scaled depth estimate is on the order of 0.1 m/kg1/3. For the yield estimate, the mean absolute percentage error is less than 10% for both training and validation datasets. The main challenge of using machine learning for yield estimate is a small number of calibration explosions with known yields available for the ANN training. In the future, the developed approach can be further improved by training the ANNs with larger datasets, as more explosion data become available.

The chiral recognition ability of the surface of α-NiSO4·6H2O was found using gas and liquid chromatography.

This review discusses the physical and physicochemical properties of polyaniline (PANI) derivatives. The most important methods for the preparation of functionalized polyanilines are presented. The presence of various substituents in its structure changes the polymer characteristics significantly due to steric and electronic effects of the functional groups. This review describes the relationship between the properties of functionalized polyanilines depending on the nature, number and position of the substituents at the aromatic ring.

Tarefas de geometria dinâmica têm um papel fundamental no ensino e aprendizagem de matemática, pois podem ampliar as possibilidades de abordagem dos conceitos geométricos estudados na Educação Básica. Assim, o objetivo deste artigo foi o de apresentar e caracterizar tarefas de geometria dinâmica que envolvem o uso de Objetos de Aprendizagem (OA) para a exploração e a investigação de conceitos geométricos. As tarefas foram elaboradas considerando os princípios metodológicos teorizados por Powell e Alqahtani (2015) e Powell e Pazuch (2016) para um trabalho investigativo usando Softwares de Geometria Dinâmica (SGD). A primeira tarefa foi desenvolvida para abordar as propriedades geométricas dos quadriláteros, enquanto a segunda permite o trabalho com as transformações geométricas presentes na animação do OA, o qual foi elaborado com o software GeoGebra. Os resultados mostram que essas tarefas podem contribuir para a prática do professor que ensina geometria e deseja integrar tecnologias digitais a seu trabalho em sala de aula.

We propose two modified and rather “universal” methods for the quantitative evaluation of electrochemical noises registered in running voltammetric sensor systems in the presence of unremovable artefacts.

Abstract ChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 100 leading journals. To access a ChemInform Abstract of an article which was published elsewhere, please select a “Full Text” option. The original article is trackable via the “References” option.

Frequency: Yearly ISSN: 0015-198X eISSN: 1938-3312 https://www.tandfonline.com/doi/abs/10.1080/0015198X.2021.1947024 The Financial Analysts Journal has a history of publishing academic and practitioner articles on environmental, social, and governance (ESG) issues; many appeared decades before the terminology became common. In celebration of the 75th anniversary, the author provides brief reviews of these articles, including reflections on how the insights brought out in this collective body of work remain important today for investors’ decisions.