General Descriptor Research Articles

A simple general descriptor for rational design of graphyne-based bifunctional electrocatalysts toward hydrogen evolution and oxygen reduction reactions

The high cost and relative scarcity of platinum (Pt) restrict large-scale commercialization of fuel cells, which has spurred researchers to develop low-cost alternatives integrating with high hydrogen evolution reaction (HER) and oxygen reduction reaction (ORR) catalytic activity. Herein, we performed density functional theory (DFT) calculations to explore the electrocatalytic activity of graphyne nanotubes (GyNTs). Several GyNTs were found to be potential metal-free electrocatalysts, with both HER and ORR activity superior to Pt. Moreover, we revealed a linear relationship between the Gibbs free energy change of O2 adsorption (ΔGOOH) and binding energy of H adsorption (ΔEH), which could be attributed to the fact that both the CO bond of OOH adsorption and the CH bond of H adsorption are single bonds. Therefore, ΔEH is proposed as a general descriptor for the rational design of bifunctional graphyne materials toward HERs and ORRs. Our findings provide a simple strategy for the rational design of bifunctional materials.

Machine Learning Enables Highly Accurate Predictions of Photophysical Properties of Organic Fluorescent Materials: Emission Wavelengths and Quantum Yields.

The development of functional organic fluorescent materials calls for fast and accurate predictions of photophysical parameters for processes such as high-throughput virtual screening, while the task is challenged by the limitations of quantum mechanical calculations. We establish a database covering >4300 solvated organic fluorescent dyes with 3000 distinct compounds and develop a new machine learning approach aimed at efficient and accurate predictions of emission wavelength and photoluminescence quantum yield (PLQY). Our feature engineering has given rise to a functionalized structure descriptor (FSD) and a comprehensive general solvent descriptor (CGSD), whereby a highly black-box computational framework is realized with consistently good accuracy across different dye families, ability of describing substitution effects and solvent effects, efficiency for large-scale predictions, and workability with on-the-fly learning. Evaluations with unseen molecules suggest a remarkable mean absolute error of 0.13 for PLQY and 0.080 eV for emission energy, the latter comparable to time-dependent density functional theory (TD-DFT) calculations. An online prediction platform was constructed based on the ensemble model to make predictions in various solvents. Our statistical learning methodology will complement quantum mechanical calculations as an efficient alternative approach for the prediction of these parameters.

Discovering Effective Descriptors for CO2 Electroreduction to Predict the Catalysts with Different Selectivity

Electroreduction is a promising approach to transform CO2, a greenhouse gas, into valuable hydrocarbons and could help mitigate the global warming effect. However, the high overpotential associated with known catalysts prohibits the adoption of CO2 electroreduction at the industrial scale. Finding an efficient catalyst is the key to overcome this hurdle. Understanding the factors that influence activity and selectivity is an essential step in systematically searching for efficient catalysts, and effective descriptors can guide such a search. In this work, we propose two effective descriptors: the CO2/CO adsorption energy descriptor and the carbon monomer mobility descriptor. The former is a general descriptor for various CO2 reduction reactions with products like CO, HCOOH, and CH4/C2H4; the latter is specific for reductions leading to CH4/C2H4. We further use these descriptors to analyze a variety of alloy catalysts and identify which among these may perform best at the reduction of CO2 to different products.

Topology and complexity of the hydrogen bond network in classical models of water

Over the years, plenty of classical interaction potentials for water have been developed and tested against structural, dynamical and thermodynamic properties. On the other hands, it has been recently observed (F. Martelli et al., ACS Nano, 14, 8616–8623, 2020) that the topology of the hydrogen bond network (HBN) is a very sensitive measure that should be considered when developing new interaction potentials. Here we report a thorough comparison of 11 popular non polarizable classical water models against their HBN, which is at the root of water properties. We probe the topology of the HBN using the ring statistics and we evaluate the quality of the network inspecting the percentage of broken and intact HBs. For each water model, we assess the tendency to develop hexagonal rings (that promote crystallization at low temperatures) and pentagonal rings (known to frustrate against crystallization at low temperatures). We then introduce the network complexity index, a general descriptor to quantify how much the topology of a given network deviates from that of the ground state, namely of hexagonal or cubic ice. Remarkably, we find that the network complexity index allows us to relate, for the first time, the dynamical properties of different water models with their underlying topology of the HBN. Our study provides a benchmark against which the performances of new models should be tested against, and introduces a general way to quantify the complexity of a network which can be transferred to other materials and that links the topology of the HBN with dynamical properties. Finally, our study introduces a new perspective that can help in rationalizing the transformations among the different phases of water and of other materials.

Optimal output regulation for heterogeneous descriptor multi-agent systems

This paper studies the cooperative optimal output regulation problem for general heterogeneous descriptor multi-agent systems under a directed topology containing a spanning tree. First, a distributed observer is presented for each agent to estimate the state information of the exogenous system. Then, the optimal state feedback control and the optimal output feedback control based on a reduced-order normal observer are designed respectively to make the output of each agent track to the reference output and minimize given performance functions simultaneously. Under some standard assumptions and employing some appropriate coordinate transformations, it’s shown that both distributed protocols can solve the optimal output regulation problems, and the optimal feedback gains are obtained by solving a set of generalized Riccati equations. Finally, two numerical examples demonstrate the effectiveness of the proposed approach.

Automatic Extraction of Roadside Traffic Facilities From Mobile Laser Scanning Point Clouds Based on Deep Belief Network

Traffic facilities extraction is of vital importance to various applications such as intelligent transportation systems, infrastructures inventory and city management related applications. Mobile laser scanning (MLS) systems provide a new technique to capture and update traffic facilities information. However, classifying raw MLS point clouds into semantic objects is still one of the most challenging and important issues. In this study, we separate the raw off-ground point clouds into individual segments and explore an object-based Deep Belief Network (DBN) architecture to detect roadside traffic facilities (trees, cars, and traffic poles) with limited labeled samples. To deal with various roadside traffic objects with different types, sizes, orientations and levels of incompleteness, we develop a simple and general multi-view feature descriptor to characterize the global feature of individual objects and extend the quantity of the training samples. Extensive experiments are employed to evaluate the validities of the proposed algorithm with six test datasets acquired by different MLS Systems. Four accuracy evaluation metrics precision, recall, quality and Fscore of trees, cars and traffic poles on the selected MLS datasets achieve (96.08%, 97.61%, 93.86%, 96.81%), (97.55%, 94.10%, 91.69%, 95.58%) and (94.39%, 97.71%, 92.37%, 95.99%), respectively. Accuracy evaluations and comparative studies prove that the proposed method has the ability of achieving the promising performance of roadside traffic facilities detection in complex urban scenes.

A General Descriptor ΔE Enables the Quantitative Development of Luminescent Materials Based on Photoinduced Electron Transfer.

Photoinduced electron transfer (PET) is one of the most important mechanisms for developing fluorescent probes and biosensors. Quantitative prediction of the quantum yields of these probes and sensors is crucial to accelerate the rational development of novel PET-based functional materials. Herein, we developed a general descriptor (ΔE) for predicting the quantum yield of PET probes, with a threshold value of ∼0.6 eV. When ΔE < ∼0.6 eV, the quantum yield is low (mostly <2%) due to the substantial activation of PET in polar environments; when ΔE > ∼0.6 eV, the quantum yield is high because of the inhibition of PET. This simple yet effective descriptor is applicable to a wide range of fluorophores, such as BODIPY, fluorescein, rhodamine, and Si-rhodamine. This ΔE descriptor enables us not only to establish new applications for existing PET probes but also to quantitatively design novel PET-based fluorophores for wash-free bioimaging and AIEgen development.

Engineering Three-Dimensional (3D) Out-of-Plane Graphene Edge Sites for Highly Selective Two-Electron Oxygen Reduction Electrocatalysis

Selective two-electron oxygen reduction reaction (ORR) offers a promising route for hydrogen peroxide synthesis, and defective sp2 carbon-based materials are attractive, low-cost electrocatalysts for this process. However, due to a wide range of possible defect structures formed during material synthesis, identification and fabrication of precise active sites remain a challenge. Here, we report a graphene edgebased electrocatalyst for two-electron ORR - nanowire-templated three-dimensional fuzzy graphene (NT3DFG). NT-3DFG exhibits excellent efficiency (onset potential of 0.79 $\pm$ 0.01 V versus RHE), selectivity (93 $\pm$ 3 % H2O2), and tunable ORR activity as a function of graphene edge site density. Using spectroscopic surface characterization and density functional theory calculations, we find that NT-3DFG edge sites are readily functionalized by carbonyl (C=O) and hydroxyl (C-OH) groups under alkaline ORR conditions. Our calculations indicate that multiple site configurations at both armchair and zigzag edges may achieve a local coordination environment that allows selective, two-electron ORR. We derive a general geometric descriptor based on the local coordination environment that provides activity predictions of graphene surface sites within ~0.1 V of computed values. Herein we combine synthesis, spectroscopy, and simulations to improve active site characterization and accelerate carbon-based electrocatalyst discovery.

Three Queries about the HOMA Index.

HOMA (Harmonic Oscillator Model of Aromaticity) is a simple, successful, and widely used geometrical aromaticity index. However, HOMA can also be used as a general molecular descriptor appropriate for any type of molecule. It reaches the global maximum for benzene, whereas the potent magnetic aromaticity NICS index has no lower or upper limits. Hence, questions arise and go beyond mere differences between the geometric and magnetic aspects of aromaticity: (1) Does a molecule of aromaticity greater than that of benzene, but undisclosed by the HOMA definition, exist? (2) Can the Kekuléne cyclohexatriene moiety with HOMA = 0 exist as a part of a larger system? (3) Can the geometrical aromaticity index be defined better? Our answer to the first query is “It is not likely enough”, to the second, “Why not define HOMA using a less mysterious molecule than cyclohexatriene?”, and to the third, “It is possible to construct another fair geometrical index, but is it better for evaluating aromaticity?” To find these answers, we have studied: (1) the HOMA and NICS indices of over 50 hexahomosubstituted benzenes, (2) the HOMA, as well as EN and GEO, indices of over 100 triply fused hexasubstituted benzenes, and (3) the HOMA and new Geometrical Auxiliary Index (GAI) , of different unsaturated and saturated, aromatic and aliphatic hydrocarbons including all alkane constitutional isomers composed of up to nine carbon atoms.

Surface Lewis Acidity of Periphery Oxide Species as a General Kinetic Descriptor for CO2 Hydrogenation to Methanol on Supported Copper Nanoparticles

Oxide-supported copper nanoparticles exhibit promising properties as catalysts for the selective hydrogenation of CO2 to methanol. Both reaction rate and selectivity depend conspicuously on the nat...

A Marxist critique of Sean Walton’s defence of the Critical Race Theory concept of ‘White supremacy’ as explaining all forms of racism, and some comments on Critical Race Theory, Black Radical and socialist futures

In the context of the ongoing debate between Critical Race Theorists and (neo-) Marxists over the Critical Race Theory concept of ‘White supremacy’, this paper extends the analysis to Black Radicalism in an attempt to further develop the neo-Marxist critique of ‘White supremacy’ deployed as a general descriptor of racism in Western societies. Specifically, the case is made that the neo-Marxist concepts of institutional racism and racialisation are better placed to understand forms of racism such as those beyond the Black/White binary, namely racism that impacts on non-Black people of colour, non-colour-coded racism and hybridist racism. Finally, futures as articulated by Critical Race Theory, Black Radicalism and neo-Marxism are addressed.

Activity Recognition for IoT Devices Using Fuzzy Spatio-Temporal Features as Environmental Sensor Fusion.

The IoT describes a development field where new approaches and trends are in constant change. In this scenario, new devices and sensors are offering higher precision in everyday life in an increasingly less invasive way. In this work, we propose the use of spatial-temporal features by means of fuzzy logic as a general descriptor for heterogeneous sensors. This fuzzy sensor representation is highly efficient and enables devices with low computing power to develop learning and evaluation tasks in activity recognition using light and efficient classifiers. To show the methodology’s potential in real applications, we deploy an intelligent environment where new UWB location devices, inertial objects, wearable devices, and binary sensors are connected with each other and describe daily human activities. We then apply the proposed fuzzy logic-based methodology to obtain spatial-temporal features to fuse the data from the heterogeneous sensor devices. A case study developed in the UJAmISmart Lab of the University of Jaen (Jaen, Spain) shows the encouraging performance of the methodology when recognizing the activity of an inhabitant using efficient classifiers.

Assembly mechanisms of soil bacterial communities in subalpine coniferous forests on the Loess Plateau, China.

Microbial community assembly is affected by trade-offs between deterministic and stochastic processes. However, the mechanisms underlying the relative influences of the two processes remain elusive. This knowledge gap limits our ability to understand the effects of community assembly processes on microbial community structures and functions. To better understand community assembly mechanisms, the community dynamics of bacterial ecological groups were investigated based on niche breadths in 23 soil plots from subalpine coniferous forests on the Loess Plateau in Shanxi, China. Here, the overall community was divided into the ecological groups that corresponded to habitat generalists, 'other taxa' and specialists. Redundancy analysis based on Bray-Curtis distances (db-RDA) and multiple regression tree (MRT) analysis indicated that soil organic carbon (SOC) was a general descriptor that encompassed the environmental gradients by which the communities responded to, because it can explain more significant variations in community diversity patterns. The three ecological groups exhibited different niche optima and degrees of specialization (i.e., niche breadths) along the SOC gradient, suggesting the presence of a gradient in tolerance for environmental heterogeneity. The inferred community assembly processes varied along the SOC gradient, wherein a transition was observed from homogenizing dispersal to variable selection that reflects increasing deterministic processes. Moreover, the ecological groups were inferred to perform different community functions that varied with community composition, structure. In conclusion, these results contribute to our understanding of the trade-offs between community assembly mechanisms and the responses of community structure and function to environmental gradients.

Predicting Catalytic Performance of Micro-Mesoporous Pt/Beta-KIT-6 Catalyst in n-Heptane Hydroisomerization Using Indexed Isomerization Factor and Experimental Verification

A Beta-KIT-6 (BK) micro-mesoporous composite material was assembled using zeolite Beta seed solution as raw material. BK composite contained both zeolite Beta units and three-dimensional cubic Ia3d mesostructure of KIT-6, which served as a catalyst support for hydroisomerization of n-heptane. The composite was characterized by a variety of techniques. Results show that catalyst Pt/HBK using the composite as the support exhibited the highest isoheptanes yield among all catalysts, which is attributed to both appropriate surface acidity and excellent mass transfer properties of Beta-KIT-6 composite. Experimental results verified that the indexed isomerization factor as a general performance descriptor could accurately predict the hydroisomerization performance of support materials.

The Butter Bump, a Magpie, John Clare

The strategies of natural history are distancing ones, and, since Michel Foucault’s The Order of Things, the genre has been the subject of powerful modes of distanced reading. A corollary, perhaps, is that any instance of natural history writing, and especially, any single entry in a natural history compilation, can seem closed to “close” reading: in the field guide as in the taxonomic systems that inform its organization, the highly codified general descriptor, structurally equivalent to every other general descriptor in the set, swallows up the situated, the particular, the near. Within this arena, John Clare’s natural history writing is, in Sara Guyer’s phrase, “hyperbolically local.” Readers have variously praised Clare’s writing on natural subjects for its decentering of an anthropomorphizing perspective, for its mimetic fidelity to an experienced local environment, for what has been described as its proto-environmental or –ecological awareness. This essay pursues the forms of local attentiveness displayed in Clare’s natural history writing in a different direction, to explore how his work engages in close, critical readings of the operations of natural history. My essay tracks Clare’s pursuit of two odd birds, “the” butter bump and “a” magpie, each of which makes a “strange noise” that reverberates through its world, traversing borders, confounding equivalencies, and sounding both the power and the limits of an already-arrived natural order of things.

Reward encourages beast.....oops, best!

Reward encourages beast.....oops, best!

Beta-MCM-41 micro-mesoporous catalysts in the hydroisomerization of n-heptane: Definition of an indexed isomerization factor as a performance descriptor

Abstract Beta-MCM-41(BM) composites consisting of Beta zeolite grown on MCM-41 type mesoporous structure were prepared. The composite materials were used as a support for stabilization of Pt. The Pt containing catalysts (Pt/HBM) were applied in n-heptane hydroisomerization reaction. The Pt/HBM catalysts showed higher n-heptane conversion and higher selectivity to isoheptanes in comparison to pure Beta and MCM-41. The results were explained by the accelerated diffusion of isoheptanes in the Pt/HBM composites, and the high concentration of acid sites with a moderate strength in the composites, which reduced the cracking of reactants and products. Based on the experimental results, an indexed isomerization factor (IIF) was deduced, which is correlated with the physical properties of the supports. The results indicated that the isoheptane yield of the catalysts is strongly dependent on the support (IIF). The general IIF descriptor for predicting the isomerization performance of support materials can provide precise guidance for the design of new micro-mesoporous composites.

One-Alpha Descriptor

Recently, one-two descriptor has been defined and it has been shown that it is a good predictor of the heat capacity at P constant (CP) and of the total surface area (TSA). In this paper, we analyze its generalizations by replacing the value 2 by arbitrary positive value . We show that these analyses may be on interest, because even good predictions of CP and TSA can be slightly improved. Furthermore, it can be expected that this more general descriptor can find a wider range of application than the original one. The extremal values of trees have been found for all values of .

SIFT-based iris recognition revisited: prerequisites, advantages and improvements

Scale-invariant feature transform (SIFT), which represents a general purpose image descriptor, has been extensively used in the field of biometric recognition. Focusing on iris biometrics, numerous SIFT-based schemes have been presented in past years, offering an alternative approach to traditional iris recognition, which are designed to extract discriminative binary feature vectors based on an analysis of pre-processed iris textures. However, the majority of proposed SIFT-based systems fails to maintain the recognition accuracy provided by generic schemes. Moreover, traditional systems outperform SIFT-based approaches with respect to other key system factors, i.e. authentication speed and storage requirement. In this work, we propose a SIFT-based iris recognition system, which circumvents the drawbacks of previous proposals. Prerequisites, derived from an analysis of the nature of iris biometric data, are utilized to construct an improved SIFT-based baseline iris recognition scheme, which operates on normalized enhanced iris textures obtained from near-infrared iris images. Subsequently, different binarization techniques are introduced and combined to obtain binary SIFT-based feature vectors from detected keypoints and their descriptors. On the CASIAv1, CASIAv4-Interval and BioSecure iris database, the proposed scheme maintains the performance of different traditional systems in terms of recognition accuracy as well as authentication speed. In addition, we show that SIFT-based features complement those extracted by traditional schemes, such that a multi-algorithm fusion at score level yields a significant gain in recognition accuracy.

Accelerated Screening of Thermoelectric Materials by First‐Principles Computations of Electron–Phonon Scattering

AbstractRecent discoveries of new materials for thermoelectric energy conversion are enabled by efficient prediction of the materials' performance from first‐principles, without empirically fitted parameters. The novel simplified approach for computing electronic transport properties is described, which achieves good accuracy and transferability while greatly reducing complexity and computation cost compared to the existing methods. The first‐principles calculations of the electron–phonon coupling demonstrate that the energy dependence of the electron relaxation time varies significantly with chemical composition and carrier concentration, suggesting that it is necessary to go beyond the commonly used approximations to screen and optimize materials' composition, carrier concentration, and microstructure. The new method is verified using high accuracy computations and validated with experimental data before applying it to screen and discover promising compositions in the space of half‐Heusler alloys. By analyzing data trends the effective electron mass is identified as the single best general descriptor determining material' performance. The Lorenz number is computed from first principles and the universality of the Wiedemann–Franz law in thermoelectrics is discussed.