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A Novel Organic Phosphonate Additive Induced Stable and Efficient Perovskite Solar Cells with Efficiency over 24% Enabled by Synergetic Crystallization Promotion and Defect Passivation.(Nano letters, 2023-09-25) [Article]Defect passivation is crucial to enhancing the performance of perovskite solar cells (PSCs). In this study, we successfully synthesized a novel organic compound named DPPO, which consists of a double phosphonate group. Subsequently, we incorporated DPPO into a perovskite solution. The presence of a P═O group interacting with undercoordinated Pb2+ yielded a perovskite film of superior crystallinity, greater crystal orientation, and smoother surface. Additionally, the addition of DPPO can passivate defect states and enhance upper layer energy level alignment, which will improve carrier extraction and prevent nonradiative recombination. Consequently, an impressive champion efficiency of 24.24% was achieved with a minimized hysteresis. Furthermore, the DPPO-modified PSCs exhibit enhanced durability when exposed to ambient conditions, maintaining 95% of the initial efficiency for 1920 h at an average relative humidity (RH) of 30%.
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Fluorine-Boosted Kinetic and Selective Molecular Sieving of C6 Derivatives.(Angewandte Chemie (International ed. in English), 2023-09-25) [Article]Porous molecular sorbents have excellent selectivity towards hydrocarbon separation with energy saving techniques. However, to realize commercialization, molecular sieving processes should be faster and more efficient compared to extended frameworks. In this work, we show that utilizing fluorine to improve the hydrophobic profile of leaning pillararenes affords a substantial kinetic selective adsorption of benzene over cyclohexane (20:1 for benzene). The crystal structure shows a porous macrocycle that acts as a perfect match for benzene in both the intrinsic and extrinsic cavities with strong interactions in the solid state. The fluorinated leaning pillararene surpasses all reported organic molecular sieves and is comparable to the extended metal organic frameworks that were previously employed for this separation such as UIO-66. Most importantly, this sieving system outperformed the well-known zeolitic imidazolate frameworks under low pressure, which opens the door to new generations of molecular sieves that can compete with extended frameworks for more sustainable hydrocarbon separation.
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Automatic Animation of Hair Blowing in Still Portrait Photos(arXiv, 2023-09-25) [Preprint]We propose a novel approach to animate human hair in a still portrait photo. Existing work has largely studied the animation of fluid elements such as water and fire. However, hair animation for a real image remains underexplored, which is a challenging problem, due to the high complexity of hair structure and dynamics. Considering the complexity of hair structure, we innovatively treat hair wisp extraction as an instance segmentation problem, where a hair wisp is referred to as an instance. With advanced instance segmentation networks, our method extracts meaningful and natural hair wisps. Furthermore, we propose a wisp-aware animation module that animates hair wisps with pleasing motions without noticeable artifacts. The extensive experiments show the superiority of our method. Our method provides the most pleasing and compelling viewing experience in the qualitative experiments and outperforms state-of-the-art still-image animation methods by a large margin in the quantitative evaluation.
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3D Printed Triaxial Nozzles Fabricated by Stereolithography to Prevent Backflow in Soft Matter Biofabrication(Accepted by Materials Science in Additive Manufacturing, 2023-09-24) [Article]Tissue engineering has been substantially impacted by 3D bioprinting due to its capacity to produce complicated structures with complex geometries that were challenging to recreate using conventional manufacturing methods. However, the nozzle design and fabrication remain a limitation within extrusion-based 3D bioprinting, restricting and compromising such technology's overall potential. The proposed nozzle design combines three Luer-Lok compatible inlets and an outlet within the printed body, eliminating manual assembly and enhancing fabrication consistency and quality. Furthermore, a finite element analysis of the fluid flow in the nozzle demonstrated the effectiveness of the nozzle to minimize backflow, compared to a traditional nozzle design. The tetrameric IIZK (Ac-Ile-IIe-Cha-Lys-NH2) and IIFK (Ac-Ile-IIe-Phe-Lys-NH2) peptide bioinks were used to 3D print a variety of 3D scaffolds of varying complexity, with good resolution and gel continuity. Hence, our work successfully demonstrates a novel design and fabrication and its potential, demonstrated ultimately via 3D bioprinting of cell-laden constructs and proving biocompatibility and cell viability post-assessed period. This study highlights the capability of the novel design, which aids the field of tissue engineering, allowing 3D extrusion-based bioprinting to be utilized to produce cell-incorporated constructions or scaffolds.
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Bayesian Parameter Inference for Partially Observed Diffusions using Multilevel Stochastic Runge-Kutta Methods(arXiv, 2023-09-24) [Preprint]We consider the problem of Bayesian estimation of static parameters associated to a partially and discretely observed diffusion process. We assume that the exact transition dynamics of the diffusion process are unavailable, even up-to an unbiased estimator and that one must time-discretize the diffusion process. In such scenarios it has been shown how one can introduce the multilevel Monte Carlo method to reduce the cost to compute posterior expected values of the parameters for a pre-specified mean square error (MSE). These afore-mentioned methods rely on upon the Euler-Maruyama discretization scheme which is well-known in numerical analysis to have slow convergence properties. We adapt stochastic Runge-Kutta (SRK) methods for Bayesian parameter estimation of static parameters for diffusions. This can be implemented in high-dimensions of the diffusion and seemingly under-appreciated in the uncertainty quantification and statistics fields. For a class of diffusions and SRK methods, we consider the estimation of the posterior expectation of the parameters. We prove that to achieve a MSE of O(ϵ2), for ϵ>0 given, the associated work is O(ϵ−2). Whilst the latter is achievable for the Milstein scheme, this method is often not applicable for diffusions in dimension larger than two. We also illustrate our methodology in several numerical examples.
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Characteristics of ammonia-hydrogen nonpremixed bluff-body-stabilized flames(Combustion and Flame, Elsevier BV, 2023-09-23) [Article]The combustion of ammonia (NH3) has received much attention over the last few years due to challenges associated with its low reactivity and the emission of nitric oxides. One way to improve the reactivity of NH3 is to blend it with (H2/N2) mixture as the product of its dissociation, before introducing it into energy systems. In this study, experimental measurements were carried out on nonpremixed bluff-body stabilized flames to better understand the flame and emission characteristics of NH3/H2/N2 flames. Four fuel mixtures at different NH3 and (H2/N2) ratios were investigated to represent different levels of ammonia cracking. Photography, planar laser-induced fluorescence of OH, thermocouples, and gas analysis techniques were used to understand flame features, reaction zone characteristics, NO, and NH3 concentration within the flame and at the exhaust. It was observed that a decrease in NH3 ratio in the mixtures resulted in longer and more stable flame with reduced thermal radiation as compared to NH3-rich fuel blends. For the highest NH3 blend studied, the flames exhibit extinction and re-ignition in the neck zone, as evidenced by OH-planar images and temperature profiles. As the H2/N2 ratio in the fuel mixture is increased, while keeping the Re constant, the momentum flux ratio (jet/co-flow) also increased resulting in a fuel-lean recirculation zone (RZ), and a shift in the maximum temperature and OH region from the outer shear layer to the inner layer next to the central jet. At levels of NH3 in the fuel mixture above 50% by volume, unburned ammonia slips through the flame and into the exhaust, and the subsequent reburn mechanism resulted in reduced NO emission. CFD simulations using Reynolds-averaged Navier-Stokes (RANS) and the flamelet–progress-variable submodel were conducted and compared with the experimental results. The CFD results helped to qualitatively describe and further explain what was observed in the experiment including the flame appearance, mixing field, and the reaction zone location in the tested flames.
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Improving Co2 and Ch4 Conversions on Exsolved Ni-Fe Alloy Perovskite Catalyst by Enlarging the Three-Phase Boundary with Minimal Rh Doping(Elsevier BV, 2023-09-23) [Preprint]Exsolved Ni-Fe alloy perovskite catalysts exhibit remarkable coking resistance in C–H activation. However, the utilization of active sites is typically incomplete, resulting in relative low catalytic activity. Herein, we investigated the minimal doping with Rh to boost the catalytic activity in dry reforming of methane by promoting exsolution and the enlargement of the three-phase boundary between the alloy, support, and reactants. The Rh influences the formation of the Ni-Fe alloy, as revealed by in-situ X-ray diffraction, and promotes the individual and collective CH4 and CO2 conversions, as revealed by packed bed reactor runs, temperature-programmed surface reactions, and operando infrared spectroscopy. A minimal 0.21 wt.% Rh addition enlarges the three-phase boundary while improving the oxygen mobility and storage. The oxygen mobility is responsible for promoting CH4dissociation and dynamic removal of carbon-containing intermediates, such that the catalyst remains stable for over 100 h under both 1 and 14 bar.
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Comparing Aerial-RIS- and Aerial-Base-Station-Aided Post-Disaster Cellular Networks(IEEE Open Journal of Vehicular Technology, Institute of Electrical and Electronics Engineers (IEEE), 2023-09-22) [Article]Reconfigurable intelligent surface (RIS) technology and its integration into existing wireless networks have recently attracted much interest. While an important use case of said technology consists in mounting RISs onto unmanned aerial vehicles (UAVs) to support the terrestrial infrastructure in post-disaster scenarios, the current literature lacks an analytical framework that captures the networks' topological aspects. Therefore, our study borrows stochastic geometry tools to estimate both the average and local coverage probability of a wireless network aided by an aerial RIS (ARIS); in particular, the surviving terrestrial base stations (TBSs) are modeled by means of an inhomogeneous Poisson point process, while the UAV is assumed to hover above the disaster epicenter. Our framework captures important aspects such as the TBSs' altitude, the fact that they may be in either line-of-sight or non-line-of-sight condition with a given node, and the Nakagami- m fading conditions of wireless links. By leveraging said aspects we accurately evaluate three possible scenarios, where TBSs are either: (i) not aided, (ii) aided by an ARIS, or (iii) aided by an aerial base station (ABS). Our selected numerical results reflect various situations, depending on parameters such as the environment's urbanization level, disaster radius, and the UAV's altitude.
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Adaptive Differentiable Grids for Cryo-Electron Tomography Reconstruction and Denoising(Bioinformatics Advances, Oxford University Press (OUP), 2023-09-22) [Article]Motivation: Tilt-series cryo-Electron Tomography is a powerful tool widely used in structural biology to study three-dimensional structures of micro-organisms, macromolecular complexes, etc. Still the reconstruction process remains an arduous task due to several challenges: The missing-wedge acquisition, sample misalignment and motion, the need to process large data, and especially a low signal-to-noise ratio (SNR). Results: Inspired by the recently introduced neural representations, we propose an adaptive learned-based representation of the density field of the captured sample. This representation consists of an octree structure, where each node represents a 3D density grid optimized from the captured projections during the training process. This optimization is performed using a loss that combines a differentiable image formation model with different regularization terms: total variation, boundary consistency, and a cross-nodes non-local constraint. The final reconstruction is obtained by interpolating the learned density grid at the desired voxel positions. The evaluation of our approach using captured data of viruses and cells shows that our proposed representation is well-adapted to handle missing-wedges, and improves the SNR of the reconstructed tomogram. The reconstruction quality is highly improved in comparison to the state-of-the-art methods, while using the lowest computing time footprint.
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Long-Lived Hot Carriers in Two-Dimensional Perovskites: The Role of Alternating Cations in Interlayer Space(ACS Energy Letters, American Chemical Society (ACS), 2023-09-22) [Article]Solar absorbers featuring prolonged hot-carrier (HC) cooling are highly desired for the development of HC solar cells. Two-dimensional (2D) hybrid perovskites are known for their exceptional stability and tunable optoelectronic properties. Nevertheless, their hot-carrier dynamics have been inadequately investigated. Here, we demonstrate ultraslow hot-carrier cooling with a lifetime >2 ns and long HC diffusion length in 2D (ACA)(MA)PbI4 (ACA = acetamidinium) with alternating cations in the interlayer space (ACI), surpassing those of 3D MAPbBr3 and 2D Ruddlesden–Popper (PEA)2PbI4. Our nonadiabatic molecular dynamics simulations with spin–orbit coupling show that the enhanced HC cooling in the ACI-phase 2D perovskite is due to multiple split-off bands and reduced electron–phonon coupling. Furthermore, the hot electrons can be efficiently extracted from (ACA)(MA)PbI4 and then transferred to the electron-transporting layer. These new insights highlight the benefit of manipulating interlayer cations in 2D perovskites as an advantageous approach to control long-lived hot carriers, thus potentially enhancing photovoltaic device performance.
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Mesophotic and Bathyal Palaemonid Shrimp Diversity of the Red Sea, with the Establishment of Two New Genera and Two New Species(Diversity, MDPI AG, 2023-09-22) [Article]The diversity and evolution of the Red Sea invertebrates in mesophotic and deep-water benthic ecosystems remain largely unexplored. The Palaemonidae is a diversified family of caridean shrimps with numerous taxa in need of taxonomic revisions based on recent molecular analyses. The Red Sea mesophotic and bathyal palaemonid shrimps are largely unstudied. During recent expeditions off the Red Sea coast of Saudi Arabia, several palaemonid specimens were collected at a depth range of 88–494 m, spanning the mesophotic and bathyal zones. This material was examined morphologically and genetically to infer phylogenetic relationships among the Red Sea taxa and several other palaemonid genera. The concordant morphological and genetic data led to the description of two new genera and two new species. Moreover, one species was recorded in the Red Sea for the first time, with a new host record, whereas three further deep-water species, which do not occur in the Red Sea, were formally transferred to a different genus. As more exploration efforts are deployed, research on the diversity and evolutionary relationships among marine invertebrates from the Red Sea will further underline the uniqueness of its mesophotic and bathyal fauna.
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AceGPT, Localizing Large Language Models in Arabic(arXiv, 2023-09-22) [Preprint]This paper explores the imperative need and methodology for developing a localized Large Language Model (LLM) tailored for Arabic, a language with unique cultural characteristics that are not adequately addressed by current mainstream models like ChatGPT. Key concerns additionally arise when considering cultural sensitivity and local values. To this end, the paper outlines a packaged solution, including further pre-training with Arabic texts, supervised fine-tuning (SFT) using native Arabic instructions and GPT-4 responses in Arabic, and reinforcement learning with AI feedback (RLAIF) using a reward model that is sensitive to local culture and values. The objective is to train culturally aware and value-aligned Arabic LLMs that can serve the diverse application-specific needs of Arabic-speaking communities. Extensive evaluations demonstrated that the resulting LLM called `\textbf{AceGPT}' is the SOTA open Arabic LLM in various benchmarks, including instruction-following benchmark (i.e., Arabic Vicuna-80 and Arabic AlpacaEval), knowledge benchmark (i.e., Arabic MMLU and EXAMs), as well as the newly-proposed Arabic cultural \& value alignment benchmark. Notably, AceGPT outperforms ChatGPT in the popular Vicuna-80 benchmark when evaluated with GPT-4, despite the benchmark's limited scale.
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Selectivity of Electrochemical CO2 Reduction on Metal Electrodes: The Role of the Surface Oxidized Layer(ACS Catalysis, American Chemical Society (ACS), 2023-09-22) [Article]In the past decade, density functional theory (DFT) calculations have been employed to study the mechanism of electrochemical CO2 reduction reactions. However, the lack of understanding of the CO2 chemisorption states, proton-coupled-electron-transfer (PCET) steps, and dynamic redox reactions of the electrode surface has limited the reliability of these simulations. The *OCHO and *COOH species are widely recognized as the key intermediates for the formic acid and carbon monoxide production, respectively. However, the comparison between the binding energies of *OCHO and *COOH cannot directly indicate the reaction trends. In this work, we propose that the energy difference between *COOH on the neutral and extra-electron substrates, in the form of [ΔG(*COOHe) – ΔG(*COOH)], can serve as a descriptor for the electrochemical CO2 reduction selectivity. In addition, the computational hydrogen electrode (CHE) model is revised by applying the previously studied charged species. The noninteger charge-transfer (NICT) model is used for the calculation of energy profile at a certain potential, which can have a good prediction of the potential-limiting step. The surface oxide of metal electrodes is found to play a key role in modulating the selectivity and improving the electron transfer to CO2.
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River interlinking alters land-atmosphere feedback and changes the Indian summer monsoon.(Nature communications, Springer Science and Business Media LLC, 2023-09-22) [Article]Massive river interlinking projects are proposed to offset observed increasing droughts and floods in India, the most populated country in the world. These projects involve water transfer from surplus to deficit river basins through reservoirs and canals without an in-depth understanding of the hydro-meteorological consequences. Here, we use causal delineation techniques, a coupled regional climate model, and multiple reanalysis datasets, and show that land-atmosphere feedbacks generate causal pathways between river basins in India. We further find that increased irrigation from the transferred water reduces mean rainfall in September by up to 12% in already water-stressed regions of India. We observe more drying in La Niña years compared to El Niño years. Reduced September precipitation can dry rivers post-monsoon, augmenting water stress across the country and rendering interlinking dysfunctional. Our findings highlight the need for model-guided impact assessment studies of large-scale hydrological projects across the globe.
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Geographic destiny trumps taxonomy in the Roundtail Chub, Gila robusta species complex (Teleostei, Leuciscidae).(Scientific reports, Springer Science and Business Media LLC, 2023-09-22) [Article]The Gila robusta species complex in the lower reaches of the Colorado River includes three nominal and contested species (G. robusta, G. intermedia, and G. nigra) originally defined by morphological and meristic characters. In subsequent investigations, none of these characters proved diagnostic, and species assignments were based on capture location. Two recent studies applied conservation genomics to assess species boundaries and reached contrasting conclusions: an ezRAD phylogenetic study resolved 5 lineages with poor alignment to species categories and proposed a single species with multiple population partitions. In contrast, a dd-RAD coalescent study concluded that the three nominal species are well-supported evolutionarily lineages. Here we developed a draft genome (~ 1.229 Gbp) to apply genome-wide coverage (10,246 SNPs) with nearly range-wide sampling of specimens (G. robusta N = 266, G. intermedia N = 241, and G. nigra N = 117) to resolve this debate. All three nominal species were polyphyletic, whereas 5 of 8 watersheds were monophyletic. AMOVA partitioned 23.1% of genetic variance among nominal species, 30.9% among watersheds, and the Little Colorado River was highly distinct (FST ranged from 0.79 to 0.88 across analyses). Likewise, DAPC identified watersheds as more distinct than species, with the Little Colorado River having 297 fixed nucleotide differences compared to zero fixed differences among the three nominal species. In every analysis, geography explains more of the observed variance than putative taxonomy, and there are no diagnostic molecular or morphological characters to justify species designation. Our analysis reconciles previous work by showing that species identities based on type location are supported by significant divergence, but natural geographic partitions show consistently greater divergence. Thus, our data confirm Gila robusta as a single polytypic species with roughly a dozen highly isolated geographic populations, providing a strong scientific basis for watershed-based future conservation.
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Transport Properties of Oil-Co2 Mixtures in Nanopores: Physics and Machine Learning Models(Elsevier BV, 2023-09-22) [Preprint]Fundamental understanding and quantitative models of the transport properties of oil-CO2 mixtures in nanopores are indispensable for physics-based models of CO2-enhanced oil recovery in unconventional oil reservoirs. This study determines the Maxwell-Stefan (M-S) diffusivities of CO2-decane (1: CO2; 2: decane /C10) mixtures in calcite nanopore with compositions relevant to CO2 Huff-n-Puff by molecular dynamics (MD) simulations. In the compositional space explored, D12 characterizing CO2-C10 interactions is relatively insensitive to composition, in contrast to that of bulk mixtures with similar compositions. D1,s characterizing CO2-wall interactions increases sharply with CO2 loading in the nanopore. In contrast, D2,s characterizing C10-wall interactions shows a nonmonotonic dependence on C10 loading. In addition, surprisingly, D2,s is negative, opposite to the expectations for dense fluid mixtures or pure decane confined in nanopores. These features of the M-S diffusivities can ultimately be traced to the fact that CO2 molecules adsorb far more strongly on pore walls than the C10 molecules, which leads to significantly heterogeneous distribution of CO2 and C10 in the nanopore and a low mobility of the adsorbed CO2 molecules. As MD simulations are computationally expensive, a non-parametric machine learning technique called the multitask Gaussian process regression method, is used to build a surrogate model to predict M-S diffusivities based on limited MD data. The surrogate model performs well in the compositional space it was trained with a relative root mean square error less than 10%.
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Oil Spill Risk Analysis For The NEOM Shoreline(2023-09-21) [Preprint]A risk analysis is conducted considering an array of release sources located around the NEOM shoreline. The sources are selected close to the coast and in neighboring regions of high marine traffic. The evolution of oil spills released by these sources is simulated using the MOHID model, driven by validated, high-resolution met-ocean fields of the Red Sea. For each source, simulations are conducted over a 4-week period, starting from first, tenth and twentieth days of each month, covering five consecutive years. A total of 48 simulations are thus conducted for each source location, adequately reflecting the variability of met-ocean conditions in the region. The risk associated with each source is described in terms of amount of oil beached, and by the elapsed time required for the spilled oil to reach the NEOM coast, extending from the Gulf of Aqaba in the North to Duba in the South. To further characterize the impact of individual sources, a finer analysis is performed by segmenting the NEOM shoreline, based on important coastal development and installation sites. For each subregion, source and release event considered, a histogram of the amount of volume beached is generated, also classifying individual events in terms of the corresponding arrival times. In addition, for each subregion considered, an inverse analysis is conducted to identify regions of dependence of the cumulative risk, estimated using the collection of all sources and events considered. The transport of oil around the NEOM shorelines is promoted by chaotic circulations and northwest winds in summer, and a dominant cyclonic eddy in winter. Hence, spills originating from release sources located close to the NEOM shorelines are characterized by large monthly variations in arrival times, ranging from less than a week to more than two weeks. Similarly, large variations in the volume fraction of beached oil, ranging from less then 50\% to more than 80\% are reported. The results of this study provide key information regarding the location of dominant oil spill risk sources, the severity of the potential release events, as well as the time frames within which mitigation actions may need to deployed.
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The Bayesian Learning Rule(Accepted by Journal of Machine Learning Research, 2023-09-21) [Article]We show that many machine-learning algorithms are specific instances of a single algorithm called the Bayesian learning rule. The rule, derived from Bayesian principles, yields a wide-range of algorithms from fields such as optimization, deep learning, and graphical models. This includes classical algorithms such as ridge regression, Newton's method, and Kalman filter, as well as modern deep-learning algorithms such as stochastic-gradient descent, RMSprop, and Dropout. The key idea in deriving such algorithms is to approximate the posterior using candidate distributions estimated by using natural gradients. Different candidate distributions result in different algorithms and further approximations to natural gradients give rise to variants of those algorithms. Our work not only unifies, generalizes, and improves existing algorithms, but also helps us design new ones.
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Unlocking massively parallel spectral proper orthogonal decompositions in the PySPOD package(arXiv, 2023-09-21) [Preprint]We propose a parallel (distributed) version of the spectral proper orthogonal decomposition (SPOD) technique. The parallel SPOD algorithm distributes the spatial dimension of the dataset preserving time. This approach is adopted to preserve the non-distributed fast Fourier transform of the data in time, thereby avoiding the associated bottlenecks. The parallel SPOD algorithm is implemented in the PySPOD library and makes use of the standard message passing interface (MPI) library, implemented in Python via mpi4py. An extensive performance evaluation of the parallel package is provided, including strong and weak scalability analyses. The open-source library allows the analysis of large datasets of interest across the scientific community. Here, we present applications in fluid dynamics and geophysics, that are extremely difficult (if not impossible) to achieve without a parallel algorithm. This work opens the path toward modal analyses of big quasi-stationary data, helping to uncover new unexplored spatiotemporal patterns.
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Solid-solvent processing of ultrathin, highly loaded mixed-matrix membrane for gas separation(Science, American Association for the Advancement of Science (AAAS), 2023-09-21) [Article]Mixed-matrix membranes (MMMs) that combine processable polymer with more permeable and selective filler have potential for molecular separation, but it remains difficult to control their interfacial compatibility and achieve ultrathin selective layers during processing, particularly at high filler loading. We present a solid-solvent processing strategy to fabricate an ultrathin MMM (thickness less than 100 nanometers) with filler loading up to 80 volume %. We used polymer as a solid solvent to dissolve metal salts to form an ultrathin precursor layer, which immobilizes the metal salt and regulates its conversion to a metal-organic framework (MOF) and provides adhesion to the MOF in the matrix. The resultant membrane exhibits fast gas-sieving properties, with hydrogen permeance and/or hydrogen–carbon dioxide selectivity one to two orders of magnitude higher than that of state-of-the-art membranes.
