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A low-communication, parallel algorithm for solving PDEs based on range decomposition
This work proposes a new, low-communication algorithm for solving PDEs on massively parallel computers. The range decomposition (RD) algorithm exposes coarse-grain parallelism by applying nested iteration and adaptive mesh refinement locally before performing a global communication step. Just a few such steps are observed to be sufficient to obtain accuracy within a small multiple of discretization error. The target applications are petascale and exascale machines, where hierarchical parallelism is required and traditional parallel numerical PDE communication patterns are costly because of message latency. The RD algorithm uses a partition of unity to equally distribute the error, and thus, the work. The computational advantages of this approach are that the decomposed problems can be solved in parallel without any communication until the partitioned solutions are summed. This offers potential advantages in the paradigm of expensive communication but very cheap computation. This paper introduces the method and explains the details of the communication step. Two performance models are developed, showing that the latency cost associated with a traditional parallel implementation of nested iteration is proportional to log(P) 2 , whereas the RD method reduces the communication latency to log(P), while maintaining similar bandwidth costs. Numerical results for two problems, Laplace and advection diffusion, demonstrate the enhanced performance, and a heuristic argument explains why the method converges quickly.
Effects of tensile loading during annealing of alnico melt spun ribbons
Conventional magnetic annealing (MA) of the permanent magnet alloy alnico involves application of an external magnetic field at temperatures within the spinodal decomposition range. This field biases the growth of the Fe-Co rich, ferromagnetic α 1 -phase in an energetically favorable 〈001〉 direction in alignment with the applied field within an Al-Ni rich, paramagnetic α 2 -phase. Utilizing a magnetic field to bias the α 1 -phase may limit alnico from reaching theoretical coercivity due to (1) the field having maximum biasing ability at temperatures near the Curie temperature where large α 1 -phase nanorods form and (2) connectivity of the α 1 -phase occurs unavoidably during MA. Both decrease the effective shape anisotropy of the α 1 -phase, thereby reducing coercivity. Herein, we explore tensile-loading as a biasing mechanism to control and optimize the final alnico nanostructure beyond that achieved by MA. Two samples of melt-spun alnico were heat-treated at 860 °C for 5 minutes: one sample was subjected to 10 MPa tensile stress for comparison with a stress-free control sample. Structural and magnetic characterization revealed that the stress-annealed ribbon sample possessed expected phase assemblages, but was distinguished by a ∼2× larger grain diameter and an elongated anisotropic α 1 -phase within grains that were oriented to a shear stress along 〈001〉 directions at an angle of ∼45° relative to the loading direction. Both types of annealing produced a similar increase in the coercivity and remanence, but a decrease in saturation magnetization.
Fluoroalkyl-functional imidazoles and imidazolium–based ionic liquids prepared via thiol-ene/yne click chemistry
A fluorinated thiol was systematically investigated against a diverse set of –ene- and –yne-bearing imidazole and imidazolium substrates in the pursuit of new cation-fluorinated, omniphobic (hydrophobic and oleophobic) ionic liquids for aerospace applications. Compounds were prepared using UV-initiated click chemistry at quantitative conversion and purified with relative ease. Seven ionic liquids were synthesized and have isolated yields ranging from 74% to >99%. Five of the new cation-fluorinated ionic liquids are hydrophobic and two are liquids at room temperature. In addition to hydrophobicity, the seven ionic liquids’ solubilities were qualitatively examined across a wide spectrum of conventional organic solvents. Of note, the majority of the prepared ionic liquids are insoluble in water and hexanes yet soluble in methanol, acetone, acetonitrile, and dichloromethane. In addition to intriguing solubility properties, the ionic liquids in this study have high densities without the incorporation of heavy atoms, ranging from 1.596 to 1.924 g cm –3 . The fluorinated salts also show good short-term thermal stability in air, with decomposition temperatures ranging from 215 °C to 320 °C. Other relevant characterization methods include spectroscopy, thermal analysis, and physical property measurements where appropriate. The X-ray crystal structure of a divalent, cation-fluorinated ionic liquid from this study was also obtained and shows unique fluorous domains in the extended structure, providing valuable insight into the materials’ physical properties. Furthermore, this work highlights a family of compounds moving towards omniphobic ionic liquids yielding potential possibilities for future applications.
Unraveling the Pyrolytic Behavior and Kinetics of Single Polymers and Plastic-Rich Municipal Solid Waste Using Thermal Analysis
Pyrolysis is a highly promising thermochemical recycling technology for converting heterogenous plastic waste into sustainable fuels in a single step. Therefore, understanding the pyrolysis mechanism is essential for enabling rational reactor design and enhancing efficient recycling techniques. In this study, the thermal degradation behaviors and corresponding kinetics of pure polymers (PE, PP, and PET) and plastic-rich MSW were examined using simultaneous thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). Experiments were carried out in the temperature range of 30-800°C with variable heating rates from 5°C/min to 20°C/min in an ultra-high purity Argon atmosphere. Our results indicated that the plastic pyrolysis was an endothermic process, with varying decomposition temperature ranges depending on their structure and composition. Various iso-conversional model-free methods (Friedman, Flynn-Wall-Ozawa, Starink, and Kissinger-Akahira-Sunose) were utilized to determine the apparent activation energy of the plastic degradation, which increased in the following order: PET (214 kJ/mol), PP (218 kJ/mol), PE (245 kJ/mol), and MSW (249 kJ/mol). Finally, Criado’s master plots were employed to identify the best-fitting reaction model and the pre-exponential factor was subsequently determined.
Microbe-iron interactions control lignin decomposition in soil
Lignin decomposition is critically linked to terrestrial carbon (C) cycle due to the enormous C mass of lignin and its importance in controlling overall rates of litter decomposition. Interactions between lignin and iron (Fe) minerals have been increasingly recognized as key mediators of lignin decomposition in experimental studies. However, we still lack a quantitative understanding of how Fe minerals interact with microbes to control lignin decomposition. Here, we leveraged experimental results from an incubation of Fe-rich soil, in which lignin decomposition rates were measured at aerobic conditions after four levels of pre-treated O2 availability, to examine microbe-Fe (MiFe) interactions in lignin decomposition with a MiFe model. We quantified how Fe redox cycling interacted with microbial activities to control lignin decomposition via data-model integration. Our results showed that the MiFe model with time-dependent growth and mortality functions better represented CO2 release from lignin decomposition (R2 ranging from 0.96 to 0.97) than models assuming either first-order or Michaelis-Menten kinetics. Reduction of Fe(III) to Fe(II) after pre-treatments with lower O2 availability stimu-lated the Fenton reaction to break down macro-molecular lignin into small molecules available to microbes. The small molecules of lignin and necromass bounded with oxidized Fe and were protected from decomposition. After 1-year incubation, the model implied that most of C stabilized with Fe minerals was derived from small molecular lignin C. Our quantitative analysis of microbe-Fe interactions sheds new light on lignin decomposition and preservation and helps improve model prediction of soil C persistence under global change.
A parsimonious modular approach to building a mechanistic belowground carbon and nitrogen model
Abstract Soil decomposition models range from simple empirical functions to those that represent physical, chemical, and biological processes. Here we develop a parsimonious, modular C and N cycle model, the Dual Arrhenius Michaelis‐Menten‐Microbial Carbon and Nitrogen Phyisology (DAMM‐MCNiP), that generates testable hypotheses regarding the effect of temperature, moisture, and substrate supply on C and N cycling. We compared this model to DAMM alone and an empirical model of heterotrophic respiration based on Harvard Forest data. We show that while different model structures explain similar amounts of variation in respiration, they differ in their ability to infer processes that affect C flux. We applied DAMM‐MCNiP to explain an observed seasonal hysteresis in the relationship between respiration and temperature and show using an exudation simulation that the strength of the priming effect depended on the stoichiometry of the inputs. Low C:N inputs stimulated priming of soil organic matter decomposition, but high C:N inputs were preferentially utilized by microbes as a C source with limited priming. The simplicity of DAMM‐MCNiP's simultaneous representations of temperature, moisture, substrate supply, enzyme activity, and microbial growth processes is unique among microbial physiology models and is sufficiently parsimonious that it could be incorporated into larger‐scale models of C and N cycling.
Monitoring the propagation of mechanical discontinuity using data-driven causal discovery and supervised learning
Mechanical wave transmission through a material is influenced by the mechanical discontinuity in the material. The propagation of embedded discontinuities can be monitored by analyzing the wave-transmission measurements recorded by a multipoint sensor system placed on the surface of the material. The proposed workflow monitors the propagation of mechanical discontinuity through three stages, namely initial, intermediate, and final stages, by using supervised learning followed by data-driven causal discovery. To the end, the workflow processes the multipoint waveform measurements resulting from a single impulse source, while considering the effects of wave attenuation, dispersion and multiple wave-propagation modes due to the discontinuity and material boundaries. Among various feature reduction techniques ranging from decomposition methods to manifold approximation methods, the features derived based on statistical parameterizations of the measured waveforms lead to reliable monitoring that is robust to changes in precision, resolution, and signal-to-noise ratio of the multipoint sensor measurements. The numbers of zero-crossing, negative-turning, and positive turning in the waveforms are the strongest causal signatures of the propagation of mechanical discontinuity. Higher order moments of the waveforms, such as variance, skewness and kurtosis, are also strong causal signatures of the propagation. Finally, the newly discovered causal signatures confirm that the statistical correlations and conventional feature rankings are not always statistically significant indicators of causality.
The Fungal Tree of Life: from Molecular Systematics to Genome-Scale Phylogenies
The kingdom Fungi is one of the more diverse clades of eukaryotes in terrestrial ecosystems, where they provide numerous ecological services ranging from decomposition of organic matter and nutrient cycling to beneficial and antagonistic associations with plants and animals. The evolutionary relationships of the kingdom have represented some of the more recalcitrant problems in systematics and phylogenetics. The advent of molecular phylogenetics, and more recently phylogenomics, has greatly advanced our understanding of the patterns and processes associated with fungal evolution, however. In this article, we review the major phyla, subphyla, and classes of the kingdom Fungi and provide brief summaries of ecologies, morphologies, and exemplar taxa. We also provide examples of how molecular phylogenetics and evolutionary genomics have advanced our understanding of fungal evolution within each of the phyla and some of the major classes. In the current classification we recognize 8 phyla, 12 subphyla, and 46 classes within the kingdom. The ancestor of fungi is inferred to be zoosporic, and zoosporic fungi comprise three lineages that are paraphyletic to the remainder of fungi. Fungi historically classified as zygomycetes do not form a monophyletic group and are paraphyletic to Ascomycota and Basidiomycota. Ascomycota and Basidiomycota are each monophyletic and collectively form the subkingdom Dikarya.
Nanometer-Resolved Spectroscopic Study Reveals the Conversion Mechanism of CaO·Al 2 O 3 ·10H 2 O to 2CaO·Al 2 O 3 ·8H 2 O and 3CaO·Al 2 O 3 ·6H 2 O at an Elevated Temperature
The main binding phases of calcium aluminate cement (CAC) concrete, CaO·Al2O3·10H2O (CAH10) and 2CaO·Al 2 O 3 ·8H 2 O (C 2 AH 8 ), slowly convert to 3CaO·Al 2 O 3 ·6H 2 O (C 3 AH 6 ) and Al(OH) 3 (AH 3 ). This reaction increasingly speeds up at a temperature higher than ~30 °C, and over time leads to significant strength loss in CAC concrete. Because of the lack of direct evidence that simultaneously probes morphological and chemical/crystallographic information, intense debate remains whether the conversion is generated by a solid-state or through-solution reaction. The conversion of CAH 10 at an elevated temperature is studied herein using synchrotron-radiation-based X-ray spectromicroscopy capable of acquiring near edge X-ray absorption fine structure data and ptychographic images with a resolution of ~15 nm. We demonstrate that, when stored at 60 °C, CAH 10 first converts to C 2 AH 8 by solid-state decomposition, followed by the through-solution formation of C 3 AH 6 . The C 3 AH 6 crystallizes from both the relics of dissolved C 2 AH 8 and from the surface of existing C 3 AH 6 crystals. The solid-state decomposition of CAH 10 occurs in multiple sites inside the CAH 10 crystals; the spatial range of each decomposition site spans a few tens of nanometers, which overcomes the kinetics barrier of ion transportation in the solid-state. Our work provides the first nanoscale crystal-chemical evidence to explain the microstructure evolution of converted CAC concrete.
Reparameterizing Litter Decomposition Using a Simplified Monte Carlo Method Improves Litter Decay Simulated by a Microbial Model and Alters Bioenergy Soil Carbon Estimates
Litter decomposition determines soil organic matter (SOM) formation and plant-available nutrient cycles. Therefore, accurate model representation of litter decomposition is critical to improving soil carbon (C) projections of bioenergy feedstocks. Soil C models that simulate microbial physiology (i.e., microbial models) are new to bioenergy agriculture, and their parameterization is often based on small datasets or manual calibration to reach benchmarks. Here, we reparameterized litter decomposition in a microbial soil C model (CORPSE - Carbon, Organisms, Rhizosphere, and Protection in the Soil Environment) using the continental-scale Long-term Inter-site Decomposition Experiment Team (LIDET) dataset which documents decomposition across a range of litter qualities over a decade. Here, we conducted a simplified Monte Carlo simulation that constrained parameter values to reduce computational costs. The LIDET-derived parameters improved modeled C and nitrogen (N) remaining, decomposition rates, and litter mean residence times as compared to Baseline parameters. We applied the LIDET litter decomposition parameters to a microbial bioenergy model (Fixation and Uptake of Nitrogen – Bioenergy Carbon, Rhizosphere, Organisms, and Protection) to examine soil C estimates generated by Baseline and LIDET parameters. LIDET parameters increased estimated soil C in bioenergy feedstocks, with even greater increases under elevated plant inputs (i.e., by increasing residue, N fertilization). This was due to the integrated effects of plant litter quantity, quality, and agricultural practices (tillage, fertilization). Collectively, we developed a simple framework for using large-scale datasets to inform the parameterization of microbial models that impacts projections of soil C for bioenergy feedstocks.
Steering the Methane Dry Reforming Reactivity of Ni/La 2 O 3 Catalysts by Controlled In Situ Decomposition of Doped La 2 NiO 4 Precursor Structures
The influence of A- and/or B-site doping of Ruddlesden-Popper perovskite materials on the crystal structure, stability, and dry reforming of methane (DRM) reactivity of specific A 2 BO 4 phases (A = La, Ba; B = Cu, Ni) has been evaluated by a combination of catalytic experiments, in situ X-ray diffraction, X-ray absorption spectroscopy (XAS), X-ray photoelectron spectroscopy (XPS), and aberration-corrected electron microscopy. At room temperature, B-site doping of La 2 NiO 4 with Cu stabilizes the orthorhombic structure ( Fmmm ) of the perovskite, while A-site doping with Ba yields a tetragonal space group ( I 4/ mmm ). We observed the orthorhombic-to-tetragonal transformation above 170 °C for La 2 Ni 0.9 Cu 0.1 O 4 and La 2 Ni 0.8 Cu 0.2 O 4 , slightly higher than for undoped La 2 NiO 4 . Loss of oxygen in interstitial sites of the tetragonal structure causes further structure transformations for all samples before decomposition in the temperature range of 400 °C-600 °C. Controlled in situ decomposition of the parent or A/B-site doped perovskite structures in a DRM mixture (CH 4 :CO 2 = 1:1) in all cases yields an active phase consisting of exsolved nanocrystalline metallic Ni particles in contact with hexagonal La 2 O 3 and a mixture of (oxy)carbonate phases (hexagonal and monoclinic La 2 O 2 CO 3 , BaCO 3 ). Differences in the catalytic activity evolve because of (i) the in situ formation of Ni-Cu alloy phases (in a composition of >7:1 = Ni:Cu) for La 2 Ni 0.9 Cu 0.1 O 4 , La 2 Ni 0.8 Cu 0.2 O 4 , and La 1.8 Ba 0.2 Ni 0.9 Cu 0.1 O 4 , (ii) the resulting Ni particle size and amount of exsolved Ni, and (iii) the inherently different reactivity of the present (oxy)carbonate species. Based on the onset temperature of catalytic DRM activity, the latter decreases in the order of La 2 Ni 0.9 Cu 0.1 O 4 ~ La 2 Ni 0.8 Cu 0.2 O 4 ≥ La 1.8 Ba 0.2 Ni 0.9 Cu 0.1 O 4 > La 2 NiO 4 > La 1.8 Ba 0.2 NiO 4 . Simple A-site doped La 1.8 Ba 0.2 NiO 4 is essentially DRM inactive. The Ni particle size can be efficiently influenced by introducing Ba into the A site of the respective Ruddlesden-Popper structures, allowing us to control the Ni particle size between 10 nm and 30 nm both for simple B-site and A-site doped structures. Hence, it is possible to steer both the extent of the metal-oxide-(oxy)carbonate interface and its chemical composition and reactivity. Counteracting the limitation of the larger Ni particle size, the activity can, however, be improved by additional Cu-doping on the B-site, enhancing the carbon reactivity. Exemplified for the La 2 NiO 4 based systems, we show how the delicate antagonistic balance of doping with Cu (rendering the La 2 NiO 4 structure less stable and suppressing coking by efficiently removing surface carbon) and Ba (rendering the La 2 NiO 4 structure more stable and forming unreactive surface or interfacial carbonates) can be used to tailor prospective DRM-active catalysts.
Observations and implications of liquid–liquid phase separation at high relative humidities in secondary organic material produced by α -pinene ozonolysis without inorganic salts
Particles consisting of secondary organic material (SOM) are abundant in the atmosphere. In order to predict the role of these particles in climate, visibility and atmospheric chemistry, information on particle phase state (i.e., single liquid, two liquids and solid) is needed. Our paper focuses on the phase state of SOM particles free of inorganic salts produced by the ozonolysis of α -pinene. Phase transitions were investigated in the laboratory using optical microscopy and theoretically using a thermodynamic model at 290 K and for relative humidities ranging from < 0.5 to 100%. In the laboratory studies, a single phase was observed from 0 to 95% relative humidity (RH) while two liquid phases were observed above 95% RH. For increasing RH, the mechanism of liquid–liquid phase separation (LLPS) was spinodal decomposition. The RH range over which two liquid phases were observed did not depend on the direction of RH change. In the modeling studies, the SOM took up very little water and was a single organic-rich phase at low RH values. At high RH, the SOM underwent LLPS to form an organic-rich phase and a water-rich phase, consistent with the laboratory studies. The presence of LLPS at high RH values can have consequences for the cloud condensation nuclei (CCN) activity of SOM particles. In the simulated Köhler curves for SOM particles, two local maxima were observed. Depending on the composition of the SOM, the first or second maximum can determine the critical supersaturation for activation. Recently researchers have observed inconsistencies between measured CCN properties of SOM particles and hygroscopic growth measured below water saturation (i.e., hygroscopic parameters measured below water saturation were inconsistent with hygroscopic parameters measured above water saturation). Furthermore, the work presented here illustrates that such inconsistencies are expected for systems with LLPS when the water uptake at subsaturated conditions represents the hygroscopicity of an organic-rich phase while the barrier for CCN activation can be determined by the second maximum in the Köhler curve when the particles are water rich.
Quantifying atmospheric carbon removal at pulp and paper mills: a life cycle assessment across system boundaries
The pulp and paper industry is a promising yet underexplored platform for large-scale carbon dioxide removal (CDR) due to its use of biogenic feedstocks and production of concentrated CO 2 emissions from point sources. This study presents the first comprehensive life cycle assessment (LCA) of retrofitting an amine-based carbon capture and storage (CCS) system into a representative virgin kraft pulp and paper mill in the Southeastern U.S. We evaluate carbon removal across five system configurations, applying both static and dynamic LCA methods under multiple functional units: CO 2 captured, biomass input, and paper output. Results show that CCS retrofits can convert a conventional mill from a net emitter into a net carbon sink, with total removal efficiencies from 17% to 92% (metric tonnes of CO 2 removed per metric tonne of CO 2 available for removal under selected boundary conditions). When carbon removal is normalized to the quantity of biogenic CO 2 captured—a narrow, gate-to-gate system boundary that considers only CCS facility emissions—removal efficiencies reached as high as 92%. The use of such narrow boundaries aligns with precedents in traditional LCA methodology, where gate-to-gate assessments are commonly applied to isolate process-level performance and allocate emissions accordingly, providing a consistent basis for comparison across technologies. Under broader cradle-to-grave boundaries—which begin tracking carbon at the point of its physical removal from the atmosphere via photosynthesis in the forest, and extend to include upstream forest operations, mill-wide emissions, and downstream product decomposition—efficiencies declined, ranging from 17% to 46% under static assumptions and dropping to 12% when accounting for dynamic biogenic carbon fluxes over time. These results underscore how system boundary definitions influence reported outcomes, while also illustrating the complementary roles of narrow and broad perspectives for different decision-making contexts.
Marine Toxicity, Biodegradability, and Rolling-Sliding Tribological Performance of Ionic Liquid-Enhanced Environmentally-Acceptable Lubricants for Tidal Turbomachinery
Environmentally acceptable lubricants (EALs) are increasingly being recognized in many fields including waterpower, hydraulics, water transport, agricultural machinery, offshore wind turbines, etc. Specifically, high-performance EALs are demanded for tidal turbomachinery to ensure high efficiency and reliability and avoid the significant risk of direct contamination of the marine ecosystem upon leaks. Here we report a new development of ionic liquid (IL)-enhanced EALs for tidal energy. One short-chain phosphonium phosphate and one short-chain ammonium phosphate ILs were used as the candidate additives and the IL-containing EALs demonstrated significantly improved lubricating performance, much lower toxicity, and increased biodegradability compared with commercial baselines. Specifically, in a rolling-sliding test simulating the operation of a model tidal turbine gearbox bearing, an EAL containing the ILs at a 0.5 wt % concentration demonstrated 40% lower friction, 45% less wear loss, substantially reduced rolling contact fatigue-induced surface damage, and one order of magnitude lower vibration noise compared with a commercial gear oil. In an EPA standard toxicity test, 90 and 70% survival of marine biota was observed when exposed to an EAL containing 5 wt % of the short-chain phosphonium phosphate and ammonium phosphate ILs, respectively, while the selected commercial gear oil and bioderived additive killed all marine biota. In a standard biodegradability test, 2 wt % addition of the phosphonium phosphate IL not only retained the EAL’s ready biodegradability but further boosted the oil decomposition from a range of 60–80% to a higher level of 80–95%. Conversely, adding the commercial bioderived additive downgraded the EAL from readily to inherently biodegradable. Furthermore, this work offers scientific insights for development of ILs as potential EAL additives for marine energy and broader applications.
Laboratory evolution in Novosphingobium aromaticivorans enables rapid catabolism of a model lignin-derived aromatic dimer
Lignin contains a variety of interunit linkages, leading to a range of potential decomposition products that can be used as carbon and energy sources by microbes. β-O-4 linkages are the most common in native lignin, and associated catabolic pathways have been well characterized. However, the fate of the mono-aromatic intermediates that result from β-O-4 dimer cleavage has not been fully elucidated. Here, we used experimental evolution to identify mutant strains of Novosphingobium aromaticivorans with improved catabolism of a model aromatic dimer containing a β-O-4 linkage, guaiacylglycerol-β-guaiacyl ether (GGE). We identified several parallel causal mutations, including a single nucleotide polymorphism in the promoter of an uncharacterized gene that roughly doubled the growth yield with GGE. We characterized the associated enzyme and demonstrated that it oxidizes an intermediate in GGE catabolism, β-hydroxypropiovanillone, to vanilloyl acetaldehyde. Identification of this enzyme and its key role in GGE catabolism furthers our understanding of catabolic pathways for lignin-derived aromatic compounds.
Thermal Decomposition of the Solid Electrolyte Interphase (SEI) on Silicon Electrodes for Lithium Ion Batteries
Here, thermal behavior of the solid electrolyte interphase (SEI) on a silicon electrode for lithium ion batteries has been investigated by TGA. In order to provide a better understanding of the thermal decomposition of the SEI on silicon, the thermal decomposition behavior of independently synthesized lithium ethylene dicarbonate (LEDC) was investigated as a model SEI. The model SEI (LEDC) has three stages of thermal decomposition. Over the temperature range of 50–300 °C, LEDC decomposes to evolve CO 2 and C 2 H 4 gases leaving lithium propionate (CH 3 CH 2 CO 2 Li) and Li 2 CO 3 as solid residues. The lithium propionate decomposes over the temperature range of 300–600 °C to evolve pentanone leaving Li 2 CO 3 as a residual solid. Finally, the Li 2 CO 3 decomposes over 600 °C to evolve CO 2 leaving Li 2 O as a residual solid. A very similar thermal decomposition process is observed for the SEI generated on cycled silicon electrodes. However, two additional thermal decomposition reactions were observed characteristic of Li x PO y F z at 300 °C and the polyimide binder at 550 °C. TGA measurements of Si electrodes after various numbers of cycles suggest that the LEDC on Si electrodes thermally decomposes during cycling to form lithium propionate and Li 2 CO 3 , resulting in increased complexity of the SEI.
The Stellar decomposition: A compact representation for simplicial complexes and beyond
Here, we introduce the Stellar decomposition, a model for efficient topological data structures over a broad range of simplicial and cell complexes. A Stellar decomposition of a complex is a collection of regions indexing the complex’s vertices and cells such that each region has sufficient information to locally reconstruct the star of its vertices, i.e., the cells incident in the region’s vertices. Stellar decompositions are general in that they can compactly represent and efficiently traverse arbitrary complexes with a manifold or non-manifold domain. They are scalable to complexes in high dimension and of large size, and they enable users to easily construct tailored application-dependent data structures using a fraction of the memory required by a corresponding global topological data structure on the complex. As a concrete realization of this model for spatially embedded complexes, we introduce the Stellar tree, which combines a nested spatial tree with a simple tuning parameter to control the number of vertices in a region. Stellar trees exploit the complex’s spatial locality by reordering vertex and cell indices according to the spatial decomposition and by compressing sequential ranges of indices. Stellar trees are competitive with state-of-the-art topological data structures for manifold simplicial complexes and offer significant improvements for cell complexes and non-manifold simplicial complexes. We conclude with a high-level description of several mesh processing and analysis applications that utilize Stellar trees to process large datasets.