Infinite Combinations Research Articles

Synthesis and characterization of magnetic carbon dots (CDs)-based hybrid material as an adsorbent for the removal of organic dye from water

In recent years, the rapid advancement of materials science and the desire for new functions has led to an enormous demand for novel materials, which could be used in various applications. As a result, hybrid materials have gained the interest of the scientific community due to the infinite combinations of individual components, which could lead to materials with unique properties. In this study, a carbon dots (CDs)-CuFe2O4 nanohybrid material was successfully prepared through a solvothermal process and utilized as an adsorbent for the removal of Congo Red (CR) dye from aqueous environment. The nanohybrid material combines interactive surface functional groups due to CDs and high magnetic saturation due to CuFe2O4 nanoparticles, resulting in enhanced selectivity towards CR dye and easy separation after utilization with an external magnet. The as-prepared material was characterized by various techniques, including XRD, micro-Raman, FT-IR, HR-TEM/EDS, N2 porosimetry, and SQUID. Finally, its capability in the removal efficiency of CR dye was investigated at different initial CR concentrations, contact times and pH values via UV–Vis spectroscopy.

Unsupervised Object-Centric Learning From Multiple Unspecified Viewpoints.

Visual scenes are extremely diverse, not only because there are infinite possible combinations of objects and backgrounds but also because the observations of the same scene may vary greatly with the change of viewpoints. When observing a multi-object visual scene from multiple viewpoints, humans can perceive the scene compositionally from each viewpoint while achieving the so-called "object constancy" across different viewpoints, even though the exact viewpoints are untold. This ability is essential for humans to identify the same object while moving and to learn from vision efficiently. It is intriguing to design models that have a similar ability. In this article, we consider a novel problem of learning compositional scene representations from multiple unspecified (i.e., unknown and unrelated) viewpoints without using any supervision and propose a deep generative model which separates latent representations into a viewpoint-independent part and a viewpoint-dependent part to solve this problem. During the inference, latent representations are randomly initialized and iteratively updated by integrating the information in different viewpoints with neural networks. Experiments on several specifically designed synthetic datasets have shown that the proposed method can effectively learn from multiple unspecified viewpoints.

Quantifying fair income distribution in Thailand.

Given a vast concern about high income inequality in Thailand as opposed to empirical findings around the world showing people's preference for fair income inequality over unfair income equality, it is therefore important to examine whether inequality in income distribution in Thailand over the past three decades is fair, and what fair inequality in income distribution in Thailand should be. To quantitatively measure fair income distribution, this study employs the fairness benchmarks that are derived from the distributions of athletes' salaries in professional sports which satisfy the concepts of distributive justice and procedural justice, the no-envy principle of fair allocation, and the general consensus or the international norm criterion of a meaningful benchmark. By using the data on quintile income shares and the income Gini index of Thailand from the National Social and Economic Development Council, this study finds that, throughout the period from 1988 to 2021, the Thai income earners in the bottom 20%, the second 20%, and the top 20% receive income shares more than the fair shares whereas those in the third 20% and the fourth 20% receive income shares less than the fair shares. Provided that there are infinite combinations of quintile income shares that can have the same value of income Gini index but only one of them is regarded as fair, this study demonstrates the use of fairness benchmarks as a practical guideline for designing policies with an aim to achieve fair income distribution in Thailand. Moreover, a comparative analysis is conducted by employing the method for estimating optimal (fair) income distribution representing feasible income equality in order to provide an alternative recommendation on what optimal (fair) income distribution characterizing feasible income equality in Thailand should be.

Informative Training Data for Efficient Property Prediction in Metal-Organic Frameworks by Active Learning.

In recent data-driven approaches to material discovery, scenarios where target quantities are expensive to compute and measure are often overlooked. In such cases, it becomes imperative to construct a training set that includes the most diverse, representative, and informative samples. Here, a novel regression tree-based active learning algorithm is employed for such a purpose. It is applied to predict the band gap and adsorption properties of metal-organic frameworks (MOFs), a novel class of materials that results from the virtually infinite combinations of their building units. Simpler and low dimensional descriptors, such as those based on stoichiometric and geometric properties, are used to compute the feature space for this model owing to their ability to better represent MOFs in the low data regime. The partitions given by a regression tree constructed on the labeled part of the data set are used to select new samples to be added to the training set, thereby limiting its size while maximizing the prediction quality. Tests on the QMOF, hMOF, and dMOF data sets reveal that our method constructs small training data sets to learn regression models that predict the target properties more efficiently than existing active learning approaches, and with lower variance. Specifically, our active learning approach is highly beneficial when labels are unevenly distributed in the descriptor space and when the label distribution is imbalanced, which is often the case for real world data. The regions defined by the tree help in revealing patterns in the data, thereby offering a unique tool to efficiently analyze complex structure-property relationships in materials and accelerate materials discovery.

A gateway to synthesis of all-optical devices of desired functionalities

I propose combining of all-optical devices to produce an all-optical device that exhibits a desired input–output (I/O) characteristic (i.e., a desired functionality). There could be infinite distinct combinations of same/different all-optical devices of same/different parameters resulting in possibility of infinite number of all-optical devices with distinct I/O characteristics, and hence, the proposal presented here opens-up a gateway to synthesis of all-optical devices of desired functionalities. To give a few of examples in support of the proposal, I show certain series and parallel combinations of nonlinear Mach-Zehnder interferometers (NMZIs) to synthesize an (i) ideal optical switch (with step like I/O characteristic), (ii) optical isolator/optical stabilizer (iii) optical pulse generator (iv) optical power filter and (v) optical limiter. The analytical findings are verified using BPM simulations in presence of losses also.

Two Iterative Methods for Sizing Pipe Diameters in Gas Distribution Networks with Loops

Closed-loop pipe systems allow the possibility of the flow of gas from both directions across each route, ensuring supply continuity in the event of a failure at one point, but their main shortcoming is in the necessity to model them using iterative methods. Two iterative methods of determining the optimal pipe diameter in a gas distribution network with closed loops are described in this paper, offering the advantage of maintaining the gas velocity within specified technical limits, even during peak demand. They are based on the following: (1) a modified Hardy Cross method with the correction of the diameter in each iteration and (2) the node-loop method, which provides a new diameter directly in each iteration. The calculation of the optimal pipe diameter in such gas distribution networks relies on ensuring mass continuity at nodes, following the first Kirchhoff law, and concluding when the pressure drops in all the closed paths are algebraically balanced, adhering to the second Kirchhoff law for energy equilibrium. The presented optimisation is based on principles developed by Hardy Cross in the 1930s for the moment distribution analysis of statically indeterminate structures. The results are for steady-state conditions and for the highest possible estimated demand of gas, while the distributed gas is treated as a noncompressible fluid due to the relatively small drop in pressure in a typical network of pipes. There is no unique solution; instead, an infinite number of potential outcomes exist, alongside infinite combinations of pipe diameters for a given fixed flow pattern that can satisfy the first and second Kirchhoff laws in the given topology of the particular network at hand.

Lateral Heterometal Junction Rectifier Fabricated by Sequential Transmetallation of Coordination Nanosheet.

Heterostructures of two-dimensional materials realise novel and enhanced physical phenomena, making them attractive research targets. Compared to inorganic materials, coordination nanosheets have virtually infinite combinations, leading to tunability of physical properties and are promising candidates for heterostructure fabrication. Although stacking of coordination materials into vertical heterostructures is widely reported, reports of lateral coordination material heterostructures are few. Here we show the successful fabrication of a seamless lateral heterojunction showing diode behaviour, by sequential and spatially limited immersion of a new metalladithiolene coordination nanosheet, Zn3 BHT, into aqueous Cu(II) and Fe(II) solutions. Upon immersion, the Zn centres in insulating Zn3 BHT are replaced by Cu or Fe ions, resulting in conductivity. The transmetallation is spatially confined, occurring only within the immersed area. We anticipate that our results will be a starting point towards exploring transmetallation of various two-dimensional materials to produce lateral heterojunctions, by providing a new and facile synthetic route.

Lateral Heterometal Junction Rectifier Fabricated by Sequential Transmetallation of Coordination Nanosheet**

AbstractHeterostructures of two‐dimensional materials realise novel and enhanced physical phenomena, making them attractive research targets. Compared to inorganic materials, coordination nanosheets have virtually infinite combinations, leading to tunability of physical properties and are promising candidates for heterostructure fabrication. Although stacking of coordination materials into vertical heterostructures is widely reported, reports of lateral coordination material heterostructures are few. Here we show the successful fabrication of a seamless lateral heterojunction showing diode behaviour, by sequential and spatially limited immersion of a new metalladithiolene coordination nanosheet, Zn3BHT, into aqueous Cu(II) and Fe(II) solutions. Upon immersion, the Zn centres in insulating Zn3BHT are replaced by Cu or Fe ions, resulting in conductivity. The transmetallation is spatially confined, occurring only within the immersed area. We anticipate that our results will be a starting point towards exploring transmetallation of various two‐dimensional materials to produce lateral heterojunctions, by providing a new and facile synthetic route.

Predicting Redox Potentials of Flow Battery-Relevant Model Electrolytes

Redox flow batteries (RFBs) are rapidly gaining traction among large-scale storage solutions for intermittent renewable energy. Given the anticipated global scale of this technology, there is an intensive ongoing search for active electrolyte species containing all earth-abundant elements. Potential RFB actives must satisfy several criteria to maximize battery performance, namely, high solubility in the RFB solvent, optimum reduction potential, and chemical stability in all redox states. Given the near infinite possible molecular combinations of the primary atoms C, H, N, O, S, and Fe, computational chemistry is an invaluable tool to accelerate the discovery of promising new RFB active compounds.Towards that end, we performed DFT calculations on two major systems: iron(III/II) tris-2,2’-bipyridine, [Fe(bpy)3]3+/2+, as a model organometallic species, and azoles as a class of small, aromatic organic compounds. For [Fe(bpy)3]3+/2+, after extensive benchmarking of various functionals, basis sets, and other parameters, we accurately predict this complex’s one-electron reduction potential. The computational protocols employed on this system serve to open up the entire class or organometallics to predictive DFT calculations for future high-throughput screening. We also performed frequency calculations in the construction of a potential energy surface of [Fe(bpy)3]3+/2+’s degradation products, with consideration of all possible spin states, during this species’ hydrolysis-initiated dimerization to µ-O-[Fe(III)(bpy)2(H2O)]2 4+. The PES is discussed in light of literature-reported mechanisms for this system.Our study on functionalized azole compounds is motivated by recent demonstrations of azo compounds for RFBs in both aqueous and non-aqueous configurations. The azo group and its related nitro to amino group-containing monomeric species offers a plethora of potentially reversible redox transformations. Here, we investigate the redox potential and solvation energies of six azole-based compounds via DFT. Amino/nitro-functionalized azoles are small, aromatic compounds with the possibility to store up to six electrons per core, which would afford unprecedented energy densities in RFBs. The findings identify several species along the nitro-azo-amino pathway that have favorable redox potentials as anolyte or catholyte species (Fig. 1), as well as potentially high water solubility. Such coupling of anolyte-catholyte species from the same class of compounds would afford a crossover-proof RFB configuration analogous to vanadium systems that has not been demonstrated for aqueous organic flow batteries. Moreover, redox potentials between cis and trans isomers of the azo species were quantified, implicating further utility of these compounds in photo-mediated cells. These results will guide experimental investigations into the feasibility of implementing azole compounds for RFBs.Figure 1. Calculated redox potentials (Latimer diagram) for various azoles, including monomeric (nitro-nitroso-hydroxylamine-amino) and dimeric (azo-type) electron transfer events. Species falling outside the -0.5 to 0.5 V range (dotted lines) implicate their potential viability as RFB anolyte (V<-0.5) or catholyte (V>0.5) redox couples. Potentials were derived from DFT-level frequency calculations (free energies) utilizing the TPSS functional, def2-TZVPP basis set, and implicit solvation (CPCM H2O, vacuum- and solvent-optimized geometries). Figure 1

The Logistic Burr XII Distribution: Properties and Applications to Income Data

We define and study the four-parameter logistic Burr XII distribution. It is obtained by inserting the three-parameter Burr XII distribution as the baseline in the logistic-X family and may be a useful alternative method to model income distribution and could be applied to other areas. We illustrate that the new distribution can have decreasing and upside-down-bathtub hazard functions and that its density function is an infinite linear combination of Burr XII densities. Some mathematical properties of the proposed model are determined, such as the quantile function, ordinary and incomplete moments, and generating function. We also obtain the maximum likelihood estimators of the model parameters and perform a Monte Carlo simulation study. Further, we present a parametric regression model based on the introduced distribution as an alternative to the location-scale regression model. The potentiality of the new distribution is illustrated by means of two applications to income data sets.

Leveraging Machine Learning for Metal-Organic Frameworks: A Perspective.

Metal-organic frameworks (MOFs) have attracted tremendous interest because of their tunable structures, functionalities, and physiochemical properties. The nearly infinite combinations of metal nodes and organic linkers have led to the synthesis of over 100,000 experimental MOFs and the construction of millions of hypothetical counterparts. It is intractable to identify the best candidates in the immense chemical space of MOFs for applications via conventional trial-to-error experiments or brute-force simulations. Over the past several years, machine learning (ML) has substantially transformed the way of MOF discovery, design, and synthesis. Driven by the abundant data from experiments or simulations, ML can not only efficiently and accurately predict MOF properties but also quantitatively derive structure-property relationships for rational design and screening. In this Perspective, we summarize recent achievements in leveraging ML for MOFs from the aspects of data acquisition, featurization, model training, and applications. Then, current challenges and new opportunities are discussed for the future exploration of ML to accelerate the development of new MOFs in this vibrant field.

Novel high entropy oxide as anode for high performance lithium-ion capacitors

A fresh type of lithium-ion battery anode called high entropy oxide (HEO) has a remarkable specific capacity and outstanding cycle performance. The advantage of HEO consists in the distinct adjustable contents along with governable chemical constitution, which makes it possible to develop novel electrode materials using infinite combinations of elements. We successfully synthesized (FeCoNiCuZn)3O4 material via a straightforward hydrothermal way in this work. At a lofty current density of 1 A g−1, obtaining an invertible capacity of 540 mAh g−1 behind 500 cycles, the unique structure of (FeCoNiCuZn)3O4 exhibits remarkable stability. We analyzed the valence states of the metal elements and further investigated their relationship with electrochemical and kinetic properties after using X-ray photoelectron spectroscopy (XPS). Subsequently, by incorporating the (FeCoNiCuZn)3O4 anode with activated carbon (AC) in a lithium-ion hybrid capacitor (LIHC), which could deliver an outstanding power density of 4000 W kg−1 and energy density of 158 Wh kg−1 while maintaining excellent capacity retention property even after 5000 cycles, the anode showed considerable performance. This is the first time HEO has been used in LIHC, as far as we are aware.

On matrix-valued Gabor frames over locally compact abelian groups

In this paper, we study Gabor frames in the matrix-valued signal space [Formula: see text], where [Formula: see text] is a locally compact abelian group which is metrizable and [Formula: see text]-compact, and [Formula: see text] is a positive integer. First, we give sufficient conditions on scalars in an infinite combination of vectors (from a given matrix-valued Gabor frame) to constitute a new frame for the space [Formula: see text]. This generalizes a result due to Aldroubi. Second, we discuss frame conditions for finite sums of matrix-valued Gabor frames. Sufficient conditions for finite sums of matrix-valued Gabor frames in terms of frame bounds are established. It is shown that the sum of images of matrix-valued Gabor frames under bounded linear operators acting on [Formula: see text] constitute a frame for the space [Formula: see text] provided operators are adjointable with respect to the matrix-valued inner product and satisfy a majorization. Finally, we show that matrix-valued Gabor frames are stable under small perturbations.

The Effects of Ionic Liquid Addition on the Heat of Polymerization of Additive Manufacturing Resins

Ionic liquids (ILs) are utilized for a wide range of applications today due to their low vapor pressure, near infinite combination of cations and anions with varying chemical functionalities, organic and inorganic species solubility, as well as their superb thermal and chemical stabilities. In this work, four ILs are used as additives for 3D printing resins to add flexibility to the final printed parts. The ILs of interest are 1-n-butyl-3-methylimidazole bis(trifluoromethanesulfonyl)imide ([C4mim][NTf2]) and 2-(1-methylimidazolium)-ethyl methacrylate chloride ([mimEMA][Cl]), which were synthesized in-house, as well as tetraethylammonium bis(trifluoromethanesulfonyl)imide ([TEA][NTf2]) and Butyltrimethylammonium bis(trifluoromethanesulfonyl)imide ([BTMA][NTf2], which were purchased and used as received. These ILs were added to commercial Formlabs CV4 resin from 10 to 50 wt%. The change in temperature of polymerization is characterized via container-less acoustic levitation and FLIR thermography and the heat capacity is characterized with differential scanning calorimetry (DSC). From these two measurements, the enthalpy of polymerization was calculated via a simple equation relating the change in droplet temperature to the reaction enthalpy equation and compared to literature values. The degradation temperature of each was also characterized via thermal gravimetric analysis (TGA). Each IL lowered the enthalpy of polymerization while the substances with [NTf2] counterions raised the decomposition temperature and the only substance containing Cl anions, [mimEMA][Cl], lowered the decomposition temperature.

Doping effects of alkaline earth element on oxygen reduction property of high-entropy perovskite cathode for solid oxide fuel cells

The nearly infinite arrangement and combination of multiple components in high entropy materials provide a huge platform for the modification and performance regulation of new materials. It is an attractive idea to introduce the concept of high entropy into material design and improve its electrochemical performance through component regulation. In this paper, cobalt-based high-entropy perovskite oxides with different alkaline earth metal contents were successfully synthesized. The phase structure, direct current conductivity, oxygen mobility characters and electrochemical properties of these materials are studied. It is found that with the continuous substitution of rare earth elements by alkaline earth elements in high-entropy materials, the content of oxygen vacancies increases and the electrochemical performance is enhanced. The chemical oxygen diffusion coefficient (Dchem) and surface exchange coefficient (Kchem) of the high-entropy oxide La0.2Pr0.2Ba0.2Sr0.2Ca0.2CoO3 are 2.8 × 10−6 cm2 s−1 and 9.1 × 10−3 cm s−1 at 700 °C, respectively. The electrochemical impedance spectroscopy measurement results show that La0.2Pr0.2Ba0.2Sr0.2Ca0.2CoO3 has the minimum polarization resistance, which is 0.03 Ω cm2 at 700 °C in air. The anode-supported fuel cell with the high entropy oxide reaches a peak power density of 792 mW cm−2 at 700 °C and exhibits attractive long-term stability and excellent CO2 resistant properties.

Introduction to the Special Issue, Anthropometry in Design

Welcome to the special issue on anthropometry! This discussion will cover a range of differently sized topics to fit your interests. De Bruin and Castelluci discuss the problems of designing school furniture that fits students, noting that “Regarding School furniture dimensions, students are usually exposed to furniture with fixed dimensions, which makes it almost impossible to adjust to the ‘growing’ anthropometrics along their school life and neither does it accommodate multidimensional fit very well.” Griffin et al. discuss developing an “understanding [of] body dimensions in relation to how a body functions, moves, and changes” that “is fundamental to creating compatible wearable products” for aging women. Alemany et al. discuss 4D scanning, observing that “This technology is able to capture the human body surface in motion at high frequency with a high resolution” and offers “an enormous potential to advance in ergonomic design and biomechanics.” Bradtmiller describes the “nearly infinite combination of head/facial characteristics” and that “This combination of traits allows us to recognize unique individuals but increases the challenge of designing head and face products that fit a wide variety of individuals with a relatively small number of sizes.”

Diagonal fields in critical loop models

In critical loop models, there exist diagonal fields with arbitrary conformal dimensions, whose 3-point functions coincide with those of Liouville theory at c≤ 1. We study their N-point functions, which depend on the 2^{N-1} weights of topologically inequivalent loops on a sphere with N punctures. Using a numerical conformal bootstrap approach, we find that 4-point functions decompose into infinite but discrete linear combinations of conformal blocks. We conclude that diagonal fields belong to an extension of the O(n) model.

Efficiency optimization for LCC‐LC compensated inductive coupling power transfer system with load‐independent zero‐phase‐angle and constant voltage output

SummaryHigh‐order compensation for the inductive coupling power transfer (ICPT) system is preferred due to its high design freedom and excellent coil current harmonics suppression capability. Especially, the good harmonics suppression capability is helpful to implement the synchronous wireless transfer of energy and data with a pair of data communication coils integrated into the energy transfer coils. Thus, on basis of THD1 and THD2 comparison for different high‐order compensation topologies, the LCC‐LC compensated ICPT system with load‐independent zero phase angle (ZPA) and constant voltage output is discussed in this paper. Compared with other high‐order compensation topologies with constant voltage output such as LC‐LC, LC‐LCC, and LCC‐LCC, the LCC‐LC has the advantages of ZPA operation with zero voltage switching and less secondary‐side components. For a given magnetic coupler, infinite feasible combinations of compensation parameters can make the system accomplish ZPA and the same output characteristics. Thus, efficiency optimization of the LCC‐LC compensated ICPT system can be investigated by configuring the optimal compensation parameters. To validate the theoretical analysis, especially the efficiency improvement, the experimental platform with 270 V input and 270 V/3 kW output is designed and built. The comparative experimental results for three different the compensation parameters show the optimized system can improve greatly conversion efficiency over the entire load range, especially under light load.

Accelerated alloy discovery using synthetic data generation and data mining

The search for new alloys with improved properties is never ending with infinite combinations and amounts of alloying elements in the alloy. Advancements in machine learning have made navigating this enormous search space feasible. However, training the machine learning models and tuning their hyper-parameters to make accurate predictions can be time-consuming and often require high-performance computing resources. Furthermore, the quality of the predictions depend on the availability of sufficient training data. Here, we present a generic approach to accelerate alloy discovery by coupling high throughput CALPHAD calculations, synthetic data generation, and data mining. As a demonstration of the approach, we design super bainitic steels that form bainite at 200∘C in lower transformation times.

A Simple Solution for the General Fractional Ambartsumian Equation

Fractionalisation and solution of the Ambartsumian equation is considered. The general approach to fractional calculus suitable for applications in physics and engineering is described. It is shown that Liouville-type derivatives are the necessary ones, because they fully preserve backward compatibility with classical results. Such derivatives are used to define and solve the fractional Ambartsumian equation. First, a solution in terms of a slowly convergent fractional Taylor series is obtained. Then, a simple solution expressed in terms of an infinite linear combination of Mittag–Leffler functions is deduced. A fast algorithm, based on a bilinear transformation and using the fast Fourier transform, is described and demonstrated for its approximate numerical realisation.