Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

Small Angle Scattering of X-Rays

Fluid phase equilibria

Efficient Carbon Nanotube Photodiodes A single photon absorbed in a single-walled carbon nanotube device can generate multiple unbound particles carrying an electric charge. Gabor et al. (p. 1367) report that in such a device at low temperatures, excitation with light of increasing energy leads to well-defined stepwise increases in current. Interestingly, because of the unique band structure of carbon nanotubes, this behavior is analogous to particle-antiparticle creation commonly observed in high-energy particle physics. These observations point to the promise of investigations in other nanoscale carbon systems, such as graphene, and could lead to numerous applications, including highly sensitive photon detection and ultra-efficient photovoltaics. The decay of photoexcited electrons in a carbon nanotube device creates multiple pairs of charge carriers. We observed highly efficient generation of electron-hole pairs due to impact excitation in single-walled carbon nanotube p-n junction photodiodes. Optical excitation into the second electronic subband E22 leads to striking photocurrent steps in the device I-VSD characteristics that occur at voltage intervals of the band-gap energy EGAP/e. Spatially and spectrally resolved photocurrent combined with temperature-dependent studies suggest that these steps result from efficient generation of multiple electron-hole pairs from a single hot E22 carrier. This process is both of fundamental interest and relevant for applications in future ultra-efficient photovoltaic devices.

https://www.lassp.cornell.edu/lassp_data/mceuen/homepage/Publications/Gabor_Science_2009_supmat.pdf

Supporting Online Material for Extremely Efficient Multiple Electron-Hole Pair Generation in Carbon Nanotube Photodiodes

With the ever increasing demand for energy to meet the needs of growth in population and improvement in the living standards in particular in developing countries, the abundant unconventional oil reserves (about 70% of total world oil), such as heavy oil, oil/tar sands and shale oil, are playing an increasingly important role in securing global energy supply. Compared with the conventional reserves unconventional oil reserves are characterized by extremely high viscosity and density, combined with complex chemistry. As a result, petroleum production from unconventional oil reserves is much more difficult and costly with more serious environmental impacts. As a key underpinning science, understanding the interfacial phenomena involved in unconventional petroleum production, such as oil liberation from host rocks, oil–water emulsions and demulsification, is critical for developing novel processes to improve oil production while reducing GHG emission and other environmental impacts at a lower operating cost. In the past decade, significant efforts and advances have been made in applying the principles of interfacial sciences to better understand complex unconventional oil-systems, while many environmental and production challenges remain. In this critical review, the recent research findings and progress in the interfacial sciences related to unconventional petroleum production are critically reviewed. In particular, the chemistry of unconventional oils, liberation mechanisms of oil from host rocks and mechanisms of emulsion stability and destabilization in unconventional oil production systems are discussed in detail. This review also seeks to summarize the current state-of-the-art characterization techniques and brings forward the challenges and opportunities for future research in this important field of physical chemistry and petroleum.

Interfacial sciences in unconventional petroleum production: from fundamentals to applications

Estimation of the viscosity of nine nanofluids using a hybrid GMDH-type neural network system

Effect of various carbon sources on biomass and lipid production of Chlorella vulgaris during nutrient sufficient and nitrogen starvation conditions

A modified Flory-Huggins model for prediction of asphaltenes precipitation in crude oil

Prediction of asphaltene precipitation in crude oil

The objective of this study is to develop a model to determine the thermal conductivity of pure ionic liquids and ionanofluids. In order to estimate the thermal conductivity of pure ionic liquids, a group method of data handling model is proposed based on 23 ionic liquids corresponding to 216 experimental data points. The average absolute relative deviation for all studied systems was 1.79%, which is a satisfactory degree of accuracy for the proposed model. Furthermore, the Maxwell model is modified to correlate the thermal conductivity of ionanofluids as a function of temperature and volume fraction of nanoparticles. The average absolute relative deviation for this model is 0.61%. Additionally, Maxwell and modified geometric mean (mGM) models are used to evaluate the models that predict the thermal conductivity of ionanofluids. The results show that mGM is more accurate for prediction of thermal conductivity of ionanofluids.

Gholamreza Pazuki

Papers

Estimation of the viscosity of nine nanofluids using a hybrid GMDH-type neural network system

Effect of various carbon sources on biomass and lipid production of Chlorella vulgaris during nutrient sufficient and nitrogen starvation conditions

A modified Flory-Huggins model for prediction of asphaltenes precipitation in crude oil

Prediction of asphaltene precipitation in crude oil

Modeling the Thermal Conductivity of Ionic Liquids and Ionanofluids Based on a Group Method of Data Handling and Modified Maxwell Model