Volumetric Surface Research Articles

Influence of Low-pH Beverages on the Two-Body Wear of CAD/CAM Monolithic Materials.

The aim of this in vitro study is to evaluate the effect of different acidic media on volumetric wear and surface roughness of CAD/CAM monolithic materials. Forty-eight rectangular specimens were prepared using different CAD/CAM monolithic materials: nanohybrid composite (Grandio Blocks, Voco), resin-based composite (Cerasmart, GC), lithium disilicate (E-Max, Ivoclar), and high-translucency zirconia (Katana STML, Kuraray Noritake). After storage in distilled water at 37 °C for two days, the specimens were tested using a chewing machine with a stainless-steel ball as an antagonist (49N loads, 250,000 cycles). Testing was performed using distilled water, Coca-Cola, and Red Bull as abrasive media. Wear and surface roughness analyses of the CAD/CAM materials were performed using a 3D profilometer and analyzed with two-way analysis of variance and post hoc pairwise comparison procedures. Worn surfaces were examined using scanning electron microscopy. Resin-based materials suffered higher volumetric wear than ceramics (p = 0.00001). Water induced significantly less volumetric wear than the other tested solutions (p = 0.0014), independent of the material tested. High-translucency zirconia showed less surface roughness than all the other materials tested. The selection of monolithic CAD/CAM materials to restore worn dentition due to erosive processes could impact restorative therapy stability over time. Resin-based materials seem to be more influenced by the acidic environment when subjected to a two-body wear test.

Seeing under the cover with a 3D U-Net: point cloud-based weight estimation of covered patients

PurposeBody weight is a crucial parameter for patient-specific treatments, particularly in the context of proper drug dosage. Contactless weight estimation from visual sensor data constitutes a promising approach to overcome challenges arising in emergency situations. Machine learning-based methods have recently been shown to perform accurate weight estimation from point cloud data. The proposed methods, however, are designed for controlled conditions in terms of visibility and position of the patient, which limits their practical applicability. In this work, we aim to decouple accurate weight estimation from such specific conditions by predicting the weight of covered patients from voxelized point cloud data.MethodsWe propose a novel deep learning framework, which comprises two 3D CNN modules solving the given task in two separate steps. First, we train a 3D U-Net to virtually uncover the patient, i.e. to predict the patient’s volumetric surface without a cover. Second, the patient’s weight is predicted from this 3D volume by means of a 3D CNN architecture, which we optimized for weight regression.ResultsWe evaluate our approach on a lying pose dataset (SLP) under two different cover conditions. The proposed framework considerably improves on the baseline model by up to {16}{%} and reduces the gap between the accuracy of weight estimates for covered and uncovered patients by up to {52}{%}.ConclusionWe present a novel pipeline to estimate the weight of patients, which are covered by a blanket. Our approach relaxes the specific conditions that were required for accurate weight estimates by previous contactless methods and thus constitutes an important step towards fully automatic weight estimation in clinical practice.

Residual Guest-Assisted MOF-5 Powder Densification.

Powder densification for specified shaped metal-organic frameworks (MOFs) is desirable for most applications. The obtainment of such properties is challenging, one of which is the rapid crystal-to-amorphous transition (framework collapse) of MOFs under pressure. Herein, we found that the residual guests of the MOF-5 synthesis process could form binding groups based on the hydrogen-bonding networks of water. The improved processability and ease of compression, which did not promote rapid structure collapse, can be achieved in the guest-loaded MOF-5. Correspondingly, enhanced volumetric specific surface area and methane uptake of MOF-5 were obtained. This work focuses on a commonly neglected but positive function of the residual guests in MOFs, besides supporting the framework. MOFs loaded with multiple types of guests show attractive mechanical properties via guest-guest and guest-host interactions for powder densification, highlighting their commercial applications.

In vitro effect of experimental nanocomposites solutions on the prevention of dental caries around orthodontic brackets.

This study aimed to evaluate the in vitro effect of a single application of experimental nanocomposite solutions on the prevention of dental caries around orthodontic brackets. The specimens were exposed to mesoporous silica (MS) nanocomposites containing fluoride by association with titanium tetrafluoride (TiF4) or sodium fluoride (NaF). Nanocomposites also could contain calcium and groups were described as MSCaTiF4, MSTiF4, MSCaNaF, MSNaF, and controls (TiF4, and NaF). Specimens were subjected to the formation of a multispecies biofilm to generate a cariogenic challenge. After 24h, both pH and total soluble fluoride concentration of the culture medium were assessed. Mineral loss was evaluated by percentage of surface mineral loss (%SML), mineral volume variation (ΔZ) of inner enamel and polarized light microscopy (PL). Linear (Ra) and volumetric (Sa) surface roughness and scanning electronic microscopy (SEM) were used to assess enamel topography. Statistical analyses were conducted considering p<0.05. MSNaF had the highest value of culture medium pH after cariogenic challenge, similarly to MSTiF4. All nanocomposite solutions released less fluoride than their controls NaF and TiF4 (p<0.05). All nanocomposite solutions presented lower %SML compared to their respective control groups (p<0.05). Lower Ra, Sa and ΔZ were observed for experimental groups compared to TiF4 (p<0.05). The results were confirmed by PL and SEM analysis. The experimental nanocomposite solutions contributed for lower enamel demineralization around orthodontic brackets.

Geometric analysis of measurement errors in a surface metrology class with closed-loop probes

This paper presents a novel measurement error model for closed-loop surface metrology. In these surface metrology systems, the motion of the probe tip is strictly constrained on a parallel surface that is separated from the measured sample surface of constant displacement by closed-loop controllers. Examples of such metrology systems include constant signal mode scanning probe microscopes and coordinate measuring machines. The combined effect of volumetric errors and surface tracking controllers on the final measurement error was analyzed using an error model developed based on multi-body system theory and surface geometry. A comprehensive overview of the errors of a typical large-scale scanning tunneling microscopy system is provided as an example. Simulations were performed to illustrate the influence of the systematic errors on the final accuracy. Experiments were also conducted on the measurement of a typical micro-structured surface to both verify the proposed error model and establish a possible compensation scheme.

Symmetry in Seeded Metal Nanocrystal Growth

ConspectusSymmetry underpins the structure and function of the world around us and is also captured in modern nanomaterials, where changing the symmetry of a nanocrystal or the interparticle spacing and orientation of nanocrystal building blocks in a superlattice can give new function. However, the synthesis and assembly of nanocrystals have been limited largely to simple compositions and structures. It remains a grand challenge to achieve nanocrystals with compositional and structural complexity while maintaining the monodispersity required for their use. This Account will illustrate through recent examples that seeded methods enable the synthesis of compositionally and structurally complex multimetallic crystals with defined and predictable symmetries for applications in plasmonics and catalysis. This outcome arises because the barrier for heterogeneous nucleation (i.e., seeded) is lower than that of homogeneous nucleation, where seeds can serve as preferential sites for the growth of complex structures and crystal phases. Our analysis begins by considering metal overgrowth from single-crystalline seeds of different shapes and symmetries, where the kinetics of adatom addition to seeds relative to their diffusion across seeds accounts for the expressed nanocrystal shapes. These results are then compared to overgrowth from seeds with different internal structures (i.e., planar defects), where the relationships between nanocrystal size and volumetric strain energy and surface energy are discussed. A major finding from this analysis is that often the underlying symmetry of seeds can be predictably transferred to the final crystals during overgrowth processes. Consequences of this finding are the predictable syntheses of crystals with different hierarchies akin to snowcrystals as well as nanocrystals with complex compositions (e.g., quaternary nanoparticles). Yet, there are subtle aspects to seeded growth that pave a path toward examples where nanocrystal symmetry has been reduced compared to the original seeds in a controlled manner. As we found, both the concentrations of metal precursors and capping agents can impact whether symmetry is transferred or reduced during overgrowth. Examples from our laboratory will be placed in context to other reported strategies for symmetry breaking. As will be argued, understanding what conditions favor symmetry preservation versus symmetry reduction during seeded crystal growth is central to accessing next-generation crystal forms. The Account concludes by outlining synthetic challenges associated with forming nanoscale heterostructures with precise 3-D placement of different materials within a given nanocrystal as well as facet control within different material domain and interface engineering. We envision meeting these challenges through regioselective and chemoselective seeded syntheses for which a foundation is outlined herein.

A numerical study of neutrally buoyant slickwater proppant flow and transport in rough fractures

Influence of rough rock surface geometry on flow behavior for single and multiple neutrally buoyant particles in fractures is explored. Degree of roughness is evaluated by varying synthetic surfaces’ root-mean-square asperity height and fractal dimension. Influences of flow Reynolds number and multiparticle volumetric concentration are also considered. Simulated behavior of particles within fluid is performed utilizing resolved computational fluid dynamics with the Discrete Element Method. Behavior within rough fracture apertures is contrasted against behavior within smooth wall fractures. Findings show accentuation in particle transport rate for some rough fractures at intermediate particle diameter to mechanical aperture ratio values. Further narrowing of fracture mechanical aperture past these intermediate values leads to increased particle–wall collision interactions, causing attenuation of particle transport rate and eventual particle arrest. Mechanical aperture in rough fractures where particle arrest occurs is appreciably larger than particle diameter and largely depend on surface fractal dimension and asperity height root-mean-square. Multiparticle flow and transport evaluations also reveal that increased particle interactions lead to varied hydraulic aperture values. Hydraulic aperture is found to be dependent on combined effects from injection particle volumetric concentration and surface roughness at moderate mechanical aperture, with increasing importance of surface roughness as mechanical aperture narrows. Further, it is found that particle jamming depends more on fracture geometry than injected particle volumetric concentration. This work provides a unique micro-level perspective of particle slurry behavior within rough rock fracture interfaces, pointing to the shortcomings of assumed analogous smooth-walled fracture face behavioral characterizations utilized in past investigations.

Recent Advancement for the Synthesis of MXene Derivatives and Their Sensing Protocol

AbstractThe highly efficient, 2D material‐based sensing devices are well‐suited with the advanced production technology which provide the data that can be applied effectively for the monitoring of environmental and health‐related problems. Recently, MXenes considered as new blood in the form of 2D transition metal carbides (TMC), transition metal nitride or transition metal carbonitrides that are added first time to 2D nanomaterials family MXene which are compounds obtained as a result of chemical delamination of quaternary or (ternary) layered nitrides or carbides found as an outstanding member of 2D elemental materials. On behalf of rich chemistries and unique morphologies, the MXenes derivatives delivered efficient sensors, energy storage, catalysis, and water purification. The superior volumetric surface area provides improved sensor response and greater optic, electrical, and magnetic characteristics, which are important advantages in biological and biomedical applications over macroscale materials. In this review article, different synthetics strategies of Mxene derivatives are organized and discuss their availability in terms of caste, yield, physical properties, and simplicity. To keep in mind, the facile synthetic approach and stable physical properties, the Ti3C2Tx Mxene is chosen as a model Mxene derivative, and its application in the field of environmental and biological sensors is discussed in detail.

Coupled two-fluid flow and wall heat conduction modeling of nucleate pool boiling

A new CFD modeling approach is developed and applied to a numerical study of pool boiling. It is based on a coupled simultaneous solving of the two-fluid model of two-phase flow in the boiling pool and the transient heat conduction within the heated wall. The applied two-fluid model consist in mass, momentum and energy balance equations for each phase and the closure laws for interface transport due to friction and phase transition. The boiling two-fluid mixture is thermally coupled with the heated wall by the heat transfer at the bubble footprint at randomly determined discrete locations of the nucleation sites and by the conjugate heat transfer from the heated wall to the wetting liquid on the surface outside the nucleation sites. The model is validated against detailed experimental data from the literature. Sensitivity analysis is performed to evaluate the influence of pool boiling parameters and conditions on the boiling curve, such as the nucleation site density, the wetting contact angle, the wall thermal conductivity, the wall thickness, the wall volumetric or bottom surface heating and the liquid volume in the pool. Temperature transients at the bubble nucleation sites are evaluated. The two-phase mixture pattern in pool boiling and the related swell level position can be well predicted with the nonuniform heat flux determined by the discrete nucleation sites of bubble growth and conjugate heat transfer from the heated wall to wetting liquid. The developed computational method is efficient and robust, while the reliability of its prediction is sensitive to the accuracy of nucleation site density and contact angle input data, as parameters that describe the coupled thermal and physical characteristics at the fluid-wall interface.

Design, imaging and performance of 3D printed open‐cell architectures for porous electrodes: quantification of surface area and permeability

AbstractBACKGROUNDThe development of new open‐cell porous electrodes for electrochemical flow cells and reactors is demonstrated through the application of 3D printing. The properties of diverse architectures were investigated, including rectangular, circular, hexagonal and triangular cells with linear porosity grades of 10, 20 and 30 pores per inch (ppi). Specimens were digitally designed, then 3D printed in stainless steel via selective laser melting. After being examined using scanning electron microscopy, they were characterized in terms of volumetric surface area and porosity with the aid of X‐ray computed tomography. Pressure drop measurements were performed over a range of mean linear velocity and Reynolds number, allowing the estimation of Darcy's friction factor and permeability.RESULTSVolumetric surface area estimated from tomography scans was ≤36% higher than the nominal values owing to surface roughness and post‐processing algorithms. By contrast, volumetric porosity obtained by tomography agreed fully with measured values. Triangular architectures afforded additional surface area both digitally and according to tomography. The largest pressure drop was found in circular materials, the triangular ones showing the lowest. The 20 ppi triangular architecture had a volumetric surface area of ≈44.5 cm−1 and a permeability of 2.31 × 10−5 cm2.CONCLUSIONTriangular architectures were preferred as a consequence of their favourable combination of high surface area and high permeability with low mass and reduced digital complexity. This provides a strategy to initiate the optimization of 3D printed porous electrodes for electrochemical flow cells and reactors in novel and niche applications. © 2021 The Authors. Journal of Chemical Technology and Biotechnology published by John Wiley &amp; Sons Ltd on behalf of Society of Chemical Industry (SCI).

Leveraging Neural Networks and Genetic Algorithms to Refine Electrode Properties in Redox Flow Batteries

Redox flow batteries are a nascent, yet promising, energy storage technology for which widespread deployment is hampered by technical and economic challenges. A performance-determining component in the reactor, present-day electrodes are often borrowed from adjacent electrochemical technologies rather than specifically designed for use in flow batteries. A lack of structural diversity in commercial offerings, coupled with the time constraints of wet-lab experiments, render broad electrode screening infeasible without a modeling complement. Herein, an experimentally validated model of a vanadium redox flow cell is used to generate polarization data for electrodes with different macrohomogeneous properties (thickness, porosity, volumetric surface area, and kinetic rate constant). Using these data sets, we then build and train a neural network with minimal average root-mean squared testing error (17.9 ± 1.8 mA cm−2) to compute individual parameter sweeps along the cell polarization curve. Finally, we employ a genetic algorithm with the neural network to identify electrode property values for improving cell power density. While the developed framework does not supplant experimentation, it is generalizable to different redox chemistries and may inform future electrode design strategies.

Bulk-surface coupling: Derivation of two models

Motivated by various physical, cellular and ecological applications, there has been a recent resurgence of interest in studying the boundary adsorption-desorption of diffusive substances between a bulk (body) and a surface, by using “bulk-surface models” (involving volumetric densities and surface densities) or by using models involving dynamical boundary conditions for volumetric densities. The surface in the models has no thickness. However, in some real world applications, the “surface” is actually a thin membrane, having positive, albeit small thickness δ, as in the cases of thermal barrier coatings for turbine engine blades and cell membranes. In such situations, rigorous derivations for these models seem lacking and desirable. In this paper, we start with two full models each of which contains reaction-diffusion equations in the bulk and the thin membrane, respectively, with two types of reasonable transmission conditions linking the two. Then in the limit of δ→0, we obtain two effective models, with one being a bulk-surface model and the other being the dynamical boundary value problem model, from which we can also recover the surface density of the other substance. Our analysis reveals that to have such effective models, it is crucial that (i) appropriate transmission conditions are satisfied on the interface between the bulk and the thin membrane, and (ii) the diffusion tensor in the thin membrane is “optimally aligned”, which means the principal axes of the tensor are either normal or tangential to the interface.

Metal–Organic Frameworks for Xylene Separation: From Computational Screening to Machine Learning

Separation of xylene isomers is an important process in the chemical industry and there has been considerable interest in developing advanced materials for xylene separation. In this study, we synergize computational screening and machine learning to explore the selective adsorption of p-xylene over o- and m-xylene in metal–organic frameworks (MOFs). First, a large set (4764) of computation-ready experimental MOFs is screened by geometric analysis and molecular simulation. The relationships between MOF structural descriptors (void fraction, volumetric surface area, and largest cavity diameter) and separation performance metrics (adsorption capacity of p-xylene Np-xylene and selectivity of p-xylene over o- and m-xylene Sp/(m+o)) are established. Then two machine-learning methods (back-propagation neural network and decision tree), as well as particle swarm optimization, are utilized to analyze and optimize Np-xylene and Sp/(m+o). The importance of each descriptor for separation is evaluated in six different MOF data sets. In the 100 top-performing MOFs, the pore limiting diameter (PLD) and largest cavity diameter (LCD) are revealed to be key factors governing separation performance. On the basis of the threshold values of Np-xylene > 0.5 mol/kg and Sp/(m+o) > 5, seven top-performing MOFs are identified. By further incorporating framework flexibility, JIVFUQ is predicted to be the best and superior to many reported MOFs in the literature.

Densely Packed and Highly Ordered Carbon Flower Particles for High Volumetric Performance

Carbon materials with high specific surface areas are ideal support materials for many applications. However, high specific surface area and large pore volume usually render them with low bulk density, which is undesirable for applications aiming at high volumetric performance. Low bulk density stems from large interparticle‐free volume caused by inefficient random packing within the materials. Herein, a simple synthesis and assembly method is reported to afford dense carbon pellets with both high specific surface area and high bulk density, obtained from the ordered packing of low polydispersity carbon flower particles. The densely packed carbon flower particles exhibit similar specific surface area to their pressed powder analogs, while exhibiting a 66–84% increase in bulk density (0.815 g cm−3), and an ultrahigh volumetric surface area (1081 m2 cm−3). The advantages of our materials are demonstrated by supercapacitors, which achieve a high volumetric capacitance of up to 153 F cm−3. The results reinforce the importance of controlling particle size and shape for porous materials to reduce their bulk volume. The developed materials possessing high volumetric surface area will be useful for many applications, such as gas storage, supercapacitors, and batteries.

Machine learning inversion approach for soil parameters estimation over vegetated agricultural areas using a combination of water cloud model and calibrated integral equation model

Estimating volumetric soil moisture (Mv) and surface roughness (S) are the key parameters for numerous agricultural and hydrological applications. Although these two parameters can be effectively retrieved from synthetic aperture radar (SAR) data, the presence of vegetation can negatively affect the results. A method was proposed to accurately estimate Mv and S over vegetated agricultural areas. The method was based on applying the machine learning inversion approach along with SAR data to invert a combination of the parameterized water cloud model (PWCM) and the calibrated integral equation model (CIEM). The soil backscattered component in water cloud model (WCM) was generated by CIEM to be applied to the WCM parameterization and dataset simulation. Three machine learning algorithms, including the support vector regression (SVR), multi-output SVR (MSVR), and artificial neural network (ANN), were employed to model the relationship between the simulated dataset variables. The genetic algorithm was also applied to optimize the models’ parameters. The inversion technique results demonstrated that the MSVR and ANN had the highest accuracy in estimating Mv and S due to their better structures. The SMAPVEX-16 in situ dataset, along with three Sentinel-1 images, was applied to evaluate the accuracy of the WCM parameterization and the proposed method for Mv and S estimation. The accuracies of the PWCM in the VV and VH polarizations of Sentinel-1 C-band data were reasonable for VWC < 2.5 kg / m2 [root-mean-square error (RMSE) = 1.44 and 1.77 dB, respectively]. Additionally, it was observed that the trained SVR, MSVR, and ANN had similar results for different VWC values. In summary, the proposed method had high potential in vegetated agricultural areas with VWC < 2.5 kg / m2, for which the RMSEs were 4 to 7 vol. % and 0.35 to 0.46 cm depending on the VWC values in retrieving Mv and S, respectively.

Ice content and interannual water storage changes of an active rock glacier in the dry Andes of Argentina

Abstract. The quantification of volumetric ice and water content in active rock glaciers is necessary to estimate their role as water stores and contributors to runoff in dry mountain catchments. In the semi-arid to arid Andes of Argentina, active rock glaciers potentially constitute important water reservoirs due to their widespread distribution. Here however, water storage capacities and their interannual changes have so far escaped quantification in detailed field studies. Volumetric ice and water content was quantified using a petrophysical four-phase model (4PM) based on complementary electrical resistivity tomography (ERT) and seismic refraction tomography (SRT) in different positions of the Dos Lenguas rock glacier in the upper Agua Negra basin, Argentina. We derived vertical and horizontal surface changes of the Dos Lenguas rock glacier, for the periods 2016–2017 and 2017–2018 using drone-derived digital elevation models (DEMs). Interannual water storage changes of −36 mm yr−1 and +27 mm yr−1 derived from volumetric surface changes for the periods 2016–2017 and 2017–2018, respectively, indicate that significant amounts of annual precipitation can be stored in and released from the active rock glacier. Geophysical results show heterogeneous ice and water content with ice-rich permafrost and supra-, intra- and sub-permafrost water pathways at the end of the thaw period. Active layer and ice-rich permafrost control traps and pathways of shallow groundwater and thus regulate interannual storage changes and water releases from the active rock glaciers in the dry mountain catchment. The ice content of 1.7–2.0 × 109 kg in the active Dos Lenguas rock glacier represents an important long-term ice reservoir, as do other ground ice deposits in the vicinity, if compared to surface ice that covers less than 3 % of the high mountain catchment.

Thermal convection and radiation in a rotating cabinet with time-dependent heat-generated solid element and heat-conducting solid walls

Purpose This paper aims to study numerically the simulation of convective–radiative heat transfer under an effect of variable thermally generating source in a rotating square chamber. The performed analysis deals with a development of passive cooling system for the electronic devices. Design/methodology/approach The domain of interest of size H rotating at a fixed angular velocity has heat-conducting solid walls with a constant cooling temperature for the outer boundaries of the vertical walls and with thermal insulation for the outer borders of the horizontal walls. The chamber has a heater on the bottom wall with a time-dependent volumetric heat generation. The internal surfaces of the walls and the energy element are both grey diffusive emitters and reflectors. The fluid is transparent to radiation. Computational model has been written using non-dimensional parameters and worked out by the finite difference technique. The effect of the angular velocity, volumetric heat generation frequency and surface emissivity has been studied and described in detail. Findings The results show that growth of the surface emissivity leads to a diminution of the mean heater temperature, while a weak rotation can improve the energy transport for low volumetric thermal generation frequency. Originality/value An efficient computational approach has been used to work out this problem. The originality of this work is to analyze complex (conductive–convective–radiative) energy transport in a rotating system with a local element of time-dependent volumetric heat generation. To the best of the authors’ knowledge, an interaction of major heat transfer mechanisms in a rotating system with a heat-generating element is scrutinized for the first time. The results would benefit scientists and engineers to become familiar with the analysis of complex heat transfer in rotating enclosures with internal heat-generating units, and the way to predict the heat transfer rate in advanced technical systems, in industrial sectors including transportation, power generation, chemical sectors and electronics.

Modeling and analysis of a novel rotational magnetorheological abrasive flow finishing process

The present work proposes a new non-conventional machining process. The proposed method is a combination of magneto-rheological finishing and rotational abrasive flow finishing process. The modeling of the new process was done on the brass workpiece at different magnetic field strengths and at different rotational speeds to observe its effect on final surface roughness and volumetric material removal. From the results, it was inferred that the value of surface roughness decreases with the increase in rotational speed and magnetic field for the constant number of cycles. This decrease in surface roughness value authenticates the active participation of the centrifugal force and rheological behavior of polishing fluid. The present study shows that with the rotation of magneto-rheological polishing fluid, an extra component of centrifugal force adds up to the resultant force which increases the volumetric removal rate and surface finish as compared to other finishing processes.

Structural Integrity of Steel Pipeline with Clusters of Corrosion Defects.

The main goal of this paper is to evaluate the burst pressure and structural integrity of a steel pipeline based on in-line inspection results, in respect to the grouping criteria of closely spaced volumetric surface features. In the study, special attention is paid to evaluation of data provided from the diagnostics using an axial excitation magnetic flux leakage technology in respect to multiple defects grouping. Standardized clustering rules were applied to the corrosion pits taken from an in-line inspection of the gas transmission pipeline. Basic rules of interaction of pipe wall metal losses are expressed in terms of longitudinal and circumferential spacing of the features in the colony. The effect of interactions of the detected anomalies on the tube residual strength evaluated according to the Det Norske Veritas Recommended practice was investigated in the current study. In the presented case, groups of closely-spaced defects behaved similarly as individual flaws with regard to their influence on burst pressure and pipeline failure probability.

Generalized Parameters of Porous Materials as Similarity Numbers

In the multifactorial preparation of porous materials, the simultaneous/se- quential influence of a number of technological variables changes the individual parameters of the texture of the material (surface area, volume, pore size, etc.) to different values and with increase or decrease. Generalized parameters (GPs) combine these changes; new dependencies arise. GPs behave like the dimensionless similarity numbers known in science and technology (Reynolds, etc.). They split the data (phenomena) into series with similar properties, reveal special patterns and structural nuances. New GPs proposed. The average pore size is presented as the product of two GPs: the dimentionless shape factor F and pore width of unknown shape (reciprocal of the volumetric surface). Using F, for example, the SBA-15 dataset (D. Zhao, Science 1998) was split into 3 series of samples differing in synthesis temperatures, unit cell parameters, intra-wall pore volumes, pore lengths, and the ratios of wall thickness to pore size. A surprising phenomenon was discovered one of the copolymers acts in a similar way to high temperatures. The standard deviation (STD, %) of the texture parameter in the series is its serial GP. The surface topography (micropore volume per m2) is proposed; it eliminates fluctuation in material density and has a lower STD than cm3/g. Examples of the use of GPs for silica, carbon, alumina and catalysts are given. A correlation has been shown between the efficiency of some catalytic reactions (adsorption) and GPs. GPs provide new information about materials and open up new research challenges.