Experimental methods in chemical engineering: Artificial neural networks–ANNs

Artificial neural networks (ANNs) are one of the most powerful and versatile tools provided by artificial intelligence and they have now been exploited by chemical engineers for several decades in countless applications. ANNs are computational tools providing a minimalistic mathematical model of neural functions. Coupled with raw data and a learning algorithm, they can be applied to tasks such as modelling, classification, and prediction. Recently, their popularity has grown remarkably and they now constitute one of the most relevant research areas within the fields of artificial intelligence and machine learning. ANNs are large collections of simple classifiers called neurons. Chemical engineers apply them to model complex relationships, predict reactor performance, and to automate process controllers. ANNs can leverage their ability to learn and exploit large data sets, but they can also get stuck in local minima or overfit and are difficult to reverse engineer. In 2016 and 2017, ANNs were cited in 13 245 Web of Science (WoS) articles, 538 of which were in chemical engineering; the top WoS categories were electrical & electronic engineering (1615 occurrences) artificial intelligence (1253), and energy & fuels (980). The top 4 journals mentioning ANNs were Neural Computing & Applications (117), Neurocomputing (84), Energies (76), and Renewable & Sustainable Energy Reviews (76). In the near future, as larger data sets become available (and arduous to analyze), chemical engineers will be able to apply and leverage more sophisticated ANN architectures.     

Carbon nanotube-metal cluster composites: a new road to chemical sensors?

Novel carbon nanotube-metal cluster structures are proposed as prototype systems for molecular recognition at the nanoscale. Ab initio calculations show that already the bare nanotube cluster system displays some specificity because the adsorption of ammonia on a carbon nanotube-Al cluster system is easily detected electrically, while diborane adsorption does not provide an electrical signature. Since there are well-established procedures for attaching molecular receptors to metal clusters, these results provide a "proof-of-principle" for the development of novel, high-specificity molecular sensors.     

Interannual variability of surface heat fluxes in the Adriatic Sea in the period 1998-2001 and comparison with observations

Surface heat fluxes of the Adriatic Sea are estimated for the period 1998-2001 through bulk formulae with the goal to assess the uncertainties related to their estimations and to describe their interannual variability. In addition a comparison to observations is conducted. We computed the components of the sea surface heat budget by using two different operational meteorological data sets as inputs: the ECMWF operational analysis and the regional limited area model LAMBO operational forecast. Both results are consistent with previous long-term climatology and short-term analyses present in the literature. In both cases we obtained that the Adriatic Sea loses 26 W/m2 on average, that is consistent with the assessments found in the literature. Then we conducted a comparison with observations of the radiative components of the heat budget collected on offshore platforms and one coastal station. In the case of shortwave radiation, results show a little overestimation on the annual basis. Values obtained in this case are 172 W/m2 when using ECMWF data and 169 W/m2 when using LAMBO data. The use of either Schiano's or Gilman's and Garrett's corrections help to get even closer values. More difficult is to assess the comparison in the case of longwave radiation, with relative errors of an order of 10-20%.     

The role of personality in shaping social networks and mediating behavioral change

In this paper, we exploit different facets of the Friends and Family study to deal with two personality-related tasks of paramount importance for the user modeling and ubiquitous computing fields. First, we propose and validate an approach for automatic classification of personality traits based on the ego-networks’ structural characteristics. Our classification results show that (i) mobile phones-based behavioral data can be superior to survey ones for the purposes of personality classification from structural network properties and (ii) particular feature set/network type combinations promise to perform better with given personality traits. Then, we investigate the mediating role played by personality in the context of inducing behavioral change, specifically increasing daily physical activity using social strategies (social comparison and peer pressure). Our results confirm the role played by Extraversion and Neuroticism. Extroverts exposed to a social comparison strategy are positively associated with an increase in physical activity level, while they tend to decrease physical activity level if they are exposed to a peer pressure intervention strategy. Regarding Neuroticism dimension, neurotic people tend to increase their physical daily activity level if they are exposed to a social comparison strategy. Our findings may have implications in designing personality-based behavioral change strategies and suggest to incorporate users’ personality models in the implementation of persuasive systems.     

Anomalous Monism in a Digital Universe

Bermúdez (Philosophy of psychology: a contemporary introduction, Routledge, London, 2005) identifies the “Interface Problem” as the central problem in the philosophy of psychology: how commonsensical psychological explanations can be integrated with lower-level (cognitive, biological, etc.) explanations? In particular, since folk psychology is meant to provide causal explanations on a par with, say, neurobiological explanations, the question of how to understand the relation between the two layers arises naturally. Donald Davidson claimed that the interface problem is actually ill-posed and put forward his version of the “Autonomy Picture”, the view known as anomalous monism. This work reviews Davidson’s proposal in the light of digital universes: we model the key claims of the theory using cellular automata and show that Davidson’s original version of the Autonomy Picture (which differs, in some respects, from what is discussed by Bermúdez) is immune to two arguments against autonomy.     

Resistive random access memory (RRAM) technology: From material,device, selector, 3D integration to bottom-up fabrication

Emerging non-volatile memory technologies are promising due to their anticipated capacity benefits, non-volatility, and zero idle energy. One of the most promising candidates is resistive random access memory (RRAM) based on resistive switching (RS). This paper reviews the development of RS device technology including the fundamental physics, material engineering, three-dimension (3D) integration, and bottom-up fabrication. The device operation, physical mechanisms for resistive switching, reliability metrics, and memory cell selector candidates are summarized from the recent advancement in both industry and academia. Options for 3D memory array architectures are presented for the mass storage application. Finally, the potential application of bottom-up fabrication approaches for effective manufacturing is introduced.     

Infrared thermometry study of nanofluid pool boiling phenomena

Infrared thermometry was used to obtain first-of-a-kind, time- and space-resolved data for pool boiling phenomena in water-based nanofluids with diamond and silica nanoparticles at low concentration (<0.1 vol.%). In addition to macroscopic parameters like the average heat transfer coefficient and critical heat flux [CHF] value, more fundamental parameters such as the bubble departure diameter and frequency, growth and wait times, and nucleation site density [NSD] were directly measured for a thin, resistively heated, indium-tin-oxide surface deposited onto a sapphire substrate. Consistent with other nanofluid studies, the nanoparticles caused deterioration in the nucleate boiling heat transfer (by as much as 50%) and an increase in the CHF (by as much as 100%). The bubble departure frequency and NSD were found to be lower in nanofluids compared with water for the same wall superheat. Furthermore, it was found that a porous layer of nanoparticles built up on the heater surface during nucleate boiling, which improved surface wettability compared with the water-boiled surfaces. Using the prevalent nucleate boiling models, it was possible to correlate this improved surface wettability to the experimentally observed reductions in the bubble departure frequency, NSD, and ultimately to the deterioration in the nucleate boiling heat transfer and the CHF enhancement.     

Self‐Assembled Dual Dye‐Doped Nanosized Micelles for High‐Contrast Up‐Conversion Bioimaging

Sensitized triplet–triplet annihilation based photon up‐conversion (TTA‐UC) greatly improves the scope and applicability of fluorescence bioimaging by enabling anti‐Stokes detection at low powers, thus eliminating the background autofluorescence and limiting the potential damage of the living tissues. Here the authors present a facile, one‐step protocol to prepare dual dye‐doped, TTA‐UC active nanomicelles starting from the commercially available surfactant Kolliphor EL, a component of several FDA approved preparations. These nanosized micelles show an unprecedented up‐conversion yield of 6.5% under 0.1 W cm^?2 excitation intensity in an aqueous, nondeaerated dispersion. The supramolecular architecture obtained preserves the embedded dyes from oxygen quenching, allowing satisfactory anti‐Stokes fluorescence imaging of 3T3 cells. This is the first example of efficient multicomponent up‐converters prepared using highly biocompatible materials approved by the international authority, paving the way for the development of new complex and multifunctional materials for advanced theranostics.