Non-porous reference carbon for N2 (77.4 K) and Ar (87.3 K) adsorption

A new non-porous carbon material from granular olive stones has been prepared to be used as a reference material for the characterization of the pore structure of activated carbons. The high precision adsorption isotherms of nitrogen at 77.4 K and argon at 87.3 K on the newly developed sample have been measured, providing the standard data for a more accurate comparative analysis to characterize disordered porous carbons using comparative methods such as t- and αS-methods.Financial support from a Strategic Japanese–Spanish Cooperative Program: Nanotechnologies and New Materials for Environmental Challenges (PLE2009-0052). K.K. was supported by Exotic Nanocarbons, Japan Regional Innovation Strategy Program by the Excellent, JST

Novel Measurement Methods for Thermoelectric Power Generator Materials and Devices

Thermoelectric measurements are notoriously challenging. In this work, we outline new thermoelectric characterization methods that are experimentally more straightforward and provide much higher accuracy, reducing error by at least a factor of 2. Specifically, three novel measurement methodologies for thermal conductivity are detailed: steady‐state isothermal measurements, scanning hot probe, and lock‐in transient Harman technique. These three new measurement methodologies are validated using experimental measurement results from standards, as well as candidate materials for thermoelectric power generation. We review thermal conductivity measurement results from new half‐Heusler (ZrNiSn‐based) materials, as well as commercial (Bi,Sb)2(Te,Se)3 and mature PbTe samples. For devices, we show characterization of commercial (Bi,Sb)2(Te,Se)3 modules, precommercial PbTe/TAGS modules, and new high accuracy numerical device simulation of Skutterudite devices. Measurements are validated by comparison to well‐established standard reference materials, as well as evaluation of device performance, and comparison to theoretical prediction obtained using measurements of individual properties. The new measurement methodologies presented here provide a new, compelling, simple, and more accurate means of material characterization, providing better agreement with theory

Detection of the delayed condensation effect and determination of its impact on the accuracy of gas adsorption pore size distributions

Macroscopic, highly disordered, mesoporous materials present a continuing challenge for accurate pore structure characterization. The typical macroscopic variation in local average pore space descriptors means that methods capable of delivering statistically representative characterizations are required. Gas adsorption is a representative but indirect method, normally requiring assumptions about the correct model for data analysis. In this work we present a novel method to both expand the range, and obtain greater accuracy, for the information obtained from the main boundary adsorption isotherms by using a combination of data obtained for two adsorptives, namely nitrogen and argon, both before and after mercury porosimetry. The method makes use of the fact that nitrogen and argon apparently ‘see’ a different pore geometry following mercury entrapment, with argon, relatively, ‘ignoring’ new metal surfaces produced by mercury porosimetry. The new method permits the study of network and pore–pore co-operative effects during adsorption that substantially affect the accuracy of the characteristic parameters, such as modal pore size, obtained for disordered materials. These effects have been explicitly quantified, for a typical sol-gel silica catalyst support material as a case study. The technique allowed the large discrepancies between modal pore sizes obtained from standard gas adsorption and mercury thermoporometry methods to be attributed to the network-based delayed condensation effect, rather than spinodal adsorption. Once the network-based delayed condensation effect had been accounted for, the simple cylindrical pore model and macroscopic thermodynamic Kelvin-Cohan equation were then found sufficient to accurately describe adsorption in the material studied, rather than needing a more complex microscopic theory. Hence, for disordered mesoporous solids, a proper account of inter-pore interactions is more important than that of intra-pore adsorbate density distribution, to obtain accurate pore size distributions

Machine learning for the subsurface characterization at core, well, and reservoir scales

The development of machine learning techniques and the digitization of the subsurface geophysical/petrophysical measurements provides a new opportunity for the industries focusing on exploration and extraction of subsurface earth resources, such as oil, gas, coal, geothermal energy, mining, and sequestration. With more data and more computation power, the traditional methods for subsurface characterization and engineering that are adopted by these industries can be automized and improved. New phenomenon can be discovered, and new understandings may be acquired from the analysis of big data. The studies conducted in this dissertation explore the possibility of applying machine learning to improve the characterization of geological materials and geomaterials. Accurate characterization of subsurface hydrocarbon reservoirs is essential for economical oil and gas reservoir development. The characterization of reservoir formation requires the integration interpretation of data from different sources. Large-scale seismic measurements, intermediate-scale well logging measurements, and small-scale core sample measurements help engineers understand the characteristics of the hydrocarbon reservoirs. Seismic data acquisition is expensive and core samples are sparse and have limited volume. Consequently, well log acquisition provides essential information that improves seismic analysis and core analysis. However, the well logging data may be missing due to financial or operational challenges or may be contaminated due to complex downhole environment. At the near-wellbore scale, I solve the data constraint problem in the reservoir characterization by applying machine learning models to generate synthetic sonic traveltime and NMR logs that are crucial for geomechanical and pore-scale characterization, respectively. At the core scale, I solve the problems in fracture characterization by processing the multipoint sonic wave propagation measurements using machine learning to characterize the dispersion, orientation, and distribution of cracks embedded in material. At reservoir scale, I utilize reinforcement learning models to achieve automatic history matching by using a fast-marching-based reservoir simulator to estimate reservoir permeability that controls pressure transient response of the well. The application of machine learning provides new insights into traditional subsurface characterization techniques. First, by applying shallow and deep machine learning models, sonic logs and NMR T2 logs can be acquired from other easy-to-acquire well logs with high accuracy. Second, the development of the sonic wave propagation simulator enables the characterization of crack-bearing materials with the simple wavefront arrival times. Third, the combination of reinforcement learning algorithms and encapsulated reservoir simulation provides a possible solution for automatic history matching

Characterization methods for silicon photodiode and silicon sub-surface properties

This thesis considers the characterization of silicon photodiode and the applications of silicon photodiodes in precision metrology, and some aspects of the silicon material characterizations. Such material characterizations are required in the process of semiconductor device manufacturing, one example of which is the silicon photodiode manufacturing. The motivation for the research on radiometry reported in this thesis has been the development of optical metrology at the Helsinki University of Technology (HUT). Most of the applications for this research are found in the UV-metrology. Importance of the UV-metrology arises from the environmental importance of accurate gauging of optical power at these wavelengths. This thesis describes the derivation and experimental verification of simple mathematical models, based on Fresnel equations. These models have allowed significant reductions in the uncertainties of spectrophotometric and radiometric measurements, especially in the UV wavelengths. These measurements are carried out using silicon photodiode-based detection systems. The reductions achieved in the measurement uncertainties have been utilized in the detector-based realizations of optical quantities maintained as national standards at HUT. The structure and operating principle of silicon photodiodes brings up the process of manufacturing of these devices, and the material characterizations required during this process. Novel methods in machining of silicon wafers for semiconductor industry pose new challenges for these characterizations. One such challenge is the need to characterize sub-surface damage in silicon wafers, induced by abrasive machining. The measurement of the sub-surface damage in silicon was the goal set for the work on materials characterization reported here. Various potential solutions to this requirement have been studied in this thesis, some of which are based on the spectrophotometric research carried out at HUT. Complete solution to this requirement has not been found. This thesis compares a number of promising methods and combines their respective advantages in order to create a more comprehensive understanding on the subject under study.reviewe

An Unsupervised Multicode Hashing Method for Accurate and Scalable Remote Sensing Image Retrieval

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Hashing methods have recently attracted great attention for approximate nearest neighbor search in massive remote sensing (RS) image archives due to their computational and storage effectiveness. The existing hashing methods in RS represent each image with a single-hash code that is usually obtained by applying hash functions to global image representations. Such an approach may not optimally represent the complex information content of RS images. To overcome this problem, in this letter, we present a simple yet effective unsupervised method that represents each image with primitive-cluster sensitive multi-hash codes (each of which corresponds to a primitive present in the image). To this end, the proposed method consists of two main steps: 1) characterization of images by descriptors of primitive-sensitive clusters and 2) definition of multi-hash codes from the descriptors of the primitive-sensitive clusters. After obtaining multi-hash codes for each image, retrieval of images is achieved based on a multi-hash-code-matching scheme. Any hashing method that provides single-hash code can be embedded within the proposed method to provide primitive-sensitive multi-hash codes. Compared with state-of-the-art single-code hashing methods in RS, the proposed method achieves higher retrieval accuracy under the same retrieval time, and thus it is more efficient for operational applications.EC/H2020/759764/EU/Accurate and Scalable Processing of Big Data in Earth Observation/BigEart

High scale 3D modelling and orthophoto of curved masonries for a multipurpose representation, analysis and assessment

It is important nowadays to underline some relevant topics concerning the effective contribution of 3D high detailed products derived from innovation and integration of Geomatics technologies, allowing a remarkable development in descriptive metric capabilities, supporting and improving the material recording, representation, analysis and characterization about alteration of the constructive systems. Considering the relevance of the complex interdisciplinary research of these issues that move around the Cultural Heritage safeguard and due to its extreme vulnerability, these models must give a response to different problems. Primarily they has to provide complete models on which to pursue accurate morpho-dimensional documentation, and to base structural assessment, decay investigations, and consequently to underpin restoration practices and support operational workflow in CH assets monitoring. Some peculiarities of new methods for semi-automatic processing algorithms are thus evidenced, advantaging their proficiency to behave as tools for a more sustainable approach in the general process of preservation and protection. Specifically about the ancient masonries documentation, the chance of using digital products derived from very high scale models, as the detailed orthoimages projection and surfaces development offers many opportunities. Here, a late-medieval stratified dovecote tower in Verolengo (TO) with a particular trunk-conical shape had been analysed in order to reconstruct an identity and a historical and architectural framework, de facto not recognized yet. A 3D reconstruction by dense matching techniques will be presented, in the complex context that are the vertical high buildings, presenting one of the highest level of vulnerability. The importance of the 3D model availability, closely connected to dense radiometric information, has been particularly expressed in two main direction for the diagnosis both of volumetric structure assessment and the material characterization of the mixed masonries walls

Review of the Synergies Between Computational Modeling and Experimental Characterization of Materials Across Length Scales

With the increasing interplay between experimental and computational approaches at multiple length scales, new research directions are emerging in materials science and computational mechanics. Such cooperative interactions find many applications in the development, characterization and design of complex material systems. This manuscript provides a broad and comprehensive overview of recent trends where predictive modeling capabilities are developed in conjunction with experiments and advanced characterization to gain a greater insight into structure-properties relationships and study various physical phenomena and mechanisms. The focus of this review is on the intersections of multiscale materials experiments and modeling relevant to the materials mechanics community. After a general discussion on the perspective from various communities, the article focuses on the latest experimental and theoretical opportunities. Emphasis is given to the role of experiments in multiscale models, including insights into how computations can be used as discovery tools for materials engineering, rather than to "simply" support experimental work. This is illustrated by examples from several application areas on structural materials. This manuscript ends with a discussion on some problems and open scientific questions that are being explored in order to advance this relatively new field of research.Comment: 25 pages, 11 figures, review article accepted for publication in J. Mater. Sc

How efficient is an integrative approach in archaeological geophysics? Comparative case studies from Neolithic settlements in Thessaly (Central Greece)

The geophysical prospection of Neolithic tells imposes specific challenges due to the preservation and nature of the architectural context and the multiple, usually disturbed, soil strata. Contrary to the usual application of a single method, this paper deals with the advantages of using an integrated geophysical approach through the employment of various methodologies to map the Neolithic cul-tural and environmental landscape of Thessalian tells (magoules) in Central Greece. The success and failure of each method in resolving the various features of the magoules are discussed in detail, and as a whole, they demonstrate the benefits of a manifold geophysical prospection of the sites