Aerobic oxidation of arsenite to arsenate by Cu(ii-chitosan/O[subscript 2] in Fenton-like reaction, a XANES investigation
Abstract not provided
Pincus, Lauren N.↗
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Knowledge Oriented Graph Unified Transformer (KOGUT) v0.1
KOGUT — Knowledge Oriented Graph Unified Transformer KOGUT implements the Relational Graph Transformer (RelGT) architecture for knowledge graph link prediction in biological domains, with a primary focus on microbial growth media prediction. While the original RelGT (arXiv:2505.10960) targets relational tables, time series, and multi-table databases, KOGUT adapts this architecture for heterogeneous biological knowledge graphs, providing first-in-class AI predictive models for microbial cultivation. Key Adaptations Beyond Original RelGT: - Knowledge Graph Focus: Applied to biological KGs with semantic node types (taxa, chemicals, media, phenotypes, environments) versus generic relational database tables, trained on the KG-Microbe knowledge graph (1.3M entities, 2.9M edges, 24 relation types). - Multimodal Node Encoding: Integrates node labels, categories, descriptions, and synonyms from KG metadata through learned embedding layers—adapting relational column features to graph node attributes with textual semantics. - Extended K-Hop Subgraph Strategy: Optimized neighborhood sampling (3-hop default, configurable up to 200 nodes) tuned for sparse biological networks, building on the original local-global attention framework with biological relation preservation. - Biolink Predicate Preservation: Type-specific transformations for 24 biological edge semantics (occurs_in, consumes, produces, has_phenotype, subclass_of) beyond standard relational foreign keys, enabling multi-relation link prediction. - Inductive Learning Support: Enables zero-shot predictions for novel taxa through feature-based embeddings (temperature, oxygen requirements, gram stain, cell shape), extending the original transductive relational benchmark scope to uncultured microorganisms. CheapSOTA Performance Optimizations (This Distribution): - VQ-EMA Centroid Attention: Vector quantization with exponential moving average for improved global context modeling (+5-10% MRR improvement). - HDF5 Precomputed Data Loading: One-time preprocessing of k-hop subgraphs to eliminate redundant graph traversals (2-5× training speedup). - Distributed Data Parallel Training: Multi-GPU support for scaling to larger knowledge graphs (tested on 4× NVIDIA A100 GPUs at NERSC Perlmutter). - Mixed Precision Training: Automatic mixed precision (AMP) for memory efficiency and faster training. Advantages Over Standard Knowledge Graph Embedding Models: Combines RelGT's proven multi-element tokenization (features, type, hop, structure) with graph-native biological representations, enabling interpretable link prediction across heterogeneous entities that standard embedding models (TransE, RotatE, ComplEx) and table-based transformers cannot directly model. Achieves near-perfect performance on microbial growth media prediction (MRR: 0.9966, Precision@1: 0.9932, Hit@10: 1.0000) while maintaining explainability through attention-based reasoning over biological pathways. Training Data: - KG-Microbe merged knowledge graph: 1,379,337 nodes, 2,960,472 edges - 24 biological relation types including taxonomic hierarchies, metabolic interactions, phenotype associations, and environmental relationships - Primary prediction task: Growth media suitability for microbial taxa (biolink:occurs_in, 50K edges) - Multi-relation capability: Predicts links for any of the 24 relation types, including chemical consumption/production, phenotype associations, and taxonomic classification Citation: Original RelGT Architecture: Dwivedi et al., "Relational Graph Transformer", arXiv:2505.10960, 2025 KOGUT Implementation: Knowledge Oriented Graph Unified Transformer for Microbial Growth Media Prediction Developed at Lawrence Berkeley National Laboratory (LBNL) Trained on NERSC Perlmutter supercomputer
Joachimiak, Marcin [Lawrence Berkeley National Lab↗
Characterizing friction in sliding isolation bearings
Summary The force–displacement behavior of the Friction Pendulum™ (FP) bearing is a function of the coefficient of sliding friction, axial load on the bearing and effective radius of the sliding surface. The coefficient of friction varies during the course of an earthquake with sliding velocity, axial pressure and temperature at the sliding surface. The velocity and axial pressure on the bearing depend on the response of the superstructure to the earthquake shaking. The temperature at an instant in time during earthquake shaking is a function of the histories of the coefficient of friction, sliding velocity and axial pressure, and the travel path of the slider on the sliding surface. A unified framework accommodating the complex interdependence of the coefficient of friction, sliding velocity, axial pressure and temperature is presented for implementation in nonlinear response‐history analysis. Expressions to define the relationship between the coefficient of friction and sliding velocity, axial pressure, and temperature are proposed, based on available experimental data. Response‐history analyses are performed on FP bearings with a range of geometrical and liner mechanical properties and static axial pressure. Friction is described using five different models that consider the dependence of the coefficient of friction on axial pressure, sliding velocity and temperature. Frictional heating is the most important factor that influences the maximum displacement of the isolation system and floor spectral demands if the static axial pressure is high. Isolation system displacements are not significantly affected by considerations of the influence of axial pressure and velocity on the coefficient of friction. Copyright © 2014 John Wiley & Sons, Ltd.
Kumar, Manish↗
Unveiling the origins of work-hardening enhancement and mechanical instability in laser shock peened titanium
Laser shock-peening (LSP) produces complex gradients in microstructures and residual stresses, which enables fabrication of metallic engineering components with superior mechanical properties. In this work, we use non-destructive and spatially resolved high-energy X-ray diffraction (HE-XRD) to investigate the influences of LSP on microstructure, surface topography, and residual stress for high purity titanium plates. LSP is found to produce large compressive in-plane residual stresses near the peened surface which monotonically decay to zero about 2.5 mm below the surface. These properties were also tracked during in-situ tensile loading, allowing stress partitioning and work-hardening rates to be measured as a function of depth. The surface region is found to have the highest work-hardening rate and remain mechanically stable up to sample failure. In addition, a crystal rigid rotation along a transversal axis of about 27 & nbsp; was observed at large applied strain near the surface region, which is attributed to the formation of denser dispersed shear bands. Furthermore, a sudden drop in tensile stress was observed at a depth of similar to 160 mu m from the LSP surface, indicating the formation of a localized mechanical instability zone (LMIZ). The origin of this LMIZ could be attributed to the increase in shear stress induced by a load transfer from LSP surface due to the formation of denser dispersed shear bands. These observations indicate a localized transition in deformation behavior has occurred from dispersed shear banding to LMIZ and homogeneous deformation.
36 MATERIALS SCIENCE↗
Hydroclimate-coupled framework for assessing power system resilience under summer drought and climate change
Extreme drought, exacerbated by climate change, increasingly threatens power system resilience, and a systematic assessment of such impacts is challenging due to the unpredictability of drought and their associated modeling complexity. Here, to address the challenge, this research develops a hydroclimate-coupled power system resilience assessment framework that enables systematic modeling of drought and climate change impacts on generation, transmission, and demand sectors. Applying the framework to the 2025 Eastern U.S. power grid — comprising 6,055 at-risk generators — under climate-induced summer drought scenarios (including SSP126, SSP245, SSP370, and SSP585) from 2023 to 2100, the study finds that climate-induced droughts could jeopardize the power system’s reliability to a greater extent than historical events, potentially leading to widespread load shedding. More specifically, the study reveals that under the twenty-one representative drought scenarios, the loss of load expectation (LOLE) of the grid could range from 34.77 to 91.48 days per summer. The simulations indicate that implementing resilience enhancement strategies is crucial to ensure reliable system operation, which encompasses initiatives such as demand response, upgrading open cooling systems, and transmission expansion. In all, these findings underscore the urgent need for proactive planning and investment in resilient U.S. power systems to mitigate the impacts of extreme drought events.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
A Markov framework for generalized post-event systems recovery modeling: From single to multihazards
State-dependent models can be used to represent the system recovery process as a series of stochastic transitions from lower to higher functional states. However, the applications of these models have been limited in scope and there is a lack of a generalized recovery modeling framework. A generalized framework would permit a robust forecasting of systems and system-of-systems recovery under multiple hazards, and more broadly, would contribute to community disaster preparedness. This paper develops a generalized post hazard-event recovery modeling framework based on state-dependent Markov-type processes. We then apply the proposed framework to solve a spectrum of problems that range from hind-casting single-system recovery following a single hazard event to forecasting post-event trajectories under multiple hazards and modeling the recovery of a system-of-systems. First, Markov chains are used to hind-cast the observed recovery for a portfolio of buildings affected by the 2014 South Napa, California, earthquake. Next, Markov processes are used to formulate a parametric post hazard-event recovery model, which can be updated using Bayesian statistics when relevant datasets become available. Semi-Markov processes are then used to develop a more general model of single hazard recovery, which accounts for the intensity of the loading and level of damage caused by the event. Semi-Markov processes with non-renewal features are then used to account for multihazard interactions in a post-event recovery model, and applied to a case study that involves a community in Charleston, South Carolina. Lastly, Markov-type processes are combined with Bayesian networks to model the recovery of residential, commercial, educational, and industrial buildings (system-of-systems) following a hazard event. Overall, these applications demonstrate the versatility of the Markov framework towards handling recovery problems with varying levels of complexity.
42 ENGINEERING↗
Solvent Extraction Separation of Trivalent Americium from Curium and the Lanthanides
The sterically constrained, macrocyclic, aqueous soluble ligand N,N'-bis[(6-carboxy-2-pyridyl)methyl]-1,10-diaza-18-crown-6 (H2BP18C6) was investigated for separating americium from curium and all the lanthanides by solvent extraction. Pairing H2BP18C6, which favors complexation of larger f-element cations, with acidic organophosphorus extractants that favor extraction of smaller f-element cations, such as bis-(2-ethylhexyl)phosphoric acid (HDEHP) or (2-ethylhexyl)phosphonic acid mono(2-ethylhexyl) ester (HEH[EHP]), created solvent extraction systems with good Cm/Am selectivity, excellent trans-lanthanide selectivity (Kex,Lu/Kex,La = 108), but poor selectivity for Am against the lightest lanthanides. However, using an organic phase containing both a neutral extractant, N,N,N’,N’-tetra(2-ethylhexyl)diglycolamide (TEHDGA), and HEH[EHP] enabled rejection of the lightest lanthanides during loading of the organic phase from aqueous nitric acid, eliminating their interference in the americium stripping stages. In addition, although it is a macrocyclic ligand, H2BP18C6 does not significantly impede the mass transfer kinetics of the HDEHP solvent extraction system
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
3D Microstructures for Materials and Damage Models
Many challenges exist with regard to understanding and representing complex physical processes involved with ductile damage and failure in polycrystalline metallic materials. Currently, the ability to accurately predict the macroscale ductile damage and failure response of metallic materials is lacking. Research at Los Alamos National Laboratory (LANL) is aimed at building a coupled experimental and computational methodology that supports the development of predictive damage capabilities by: capturing real distributions of microstructural features from real material and implementing them as digitally generated microstructures in damage model development; and, distilling structure-property information to link microstructural details to damage evolution under a multitude of loading states.
36 MATERIALS SCIENCE↗
Understanding Nitrate Chemistry - The Key to Safer and More Efficient TRU Waste Acceptance - 19618
Investigations into the Waste Isolation Pilot Plant (WIPP) radiological release from a transuranic (TRU) waste drum concluded that the event was initiated by a thermal runaway, principally involving interactions between the polysaccharide-based kitty litter and nitric acid, which released radiological material along with hot gases and combustible materials from the drum. In this paper, the hazards of polysaccharides present as either starch based kitty litter or cellulose rags in a TRU waste drum exposed to nitric acid (and by extension metal nitrate salts) have been evaluated, based on considerations of how complex nitrogen redox cycles carry out the aqueous phase oxidation of alcohols and related organic functional groups. These exothermic reactions, coupled with a positive feedback loop inherent to nitrogen redox chemistry, can initiate an autocatalytic thermal runaway and explosive gas generation, which poses the most significant risk under ambient conditions where acidic solution may be present (in small quantities as free liquid or sorbed within a solid matrix). Nitric acid is an extremely potent oxidant but is itself remarkably inert, rarely interacting directly with oxidizable substrates and instead exerting its vast potential energy through lower oxidation state reactants such as nitrogen dioxide and nitrous acid that are generated from its slow self-decomposition. Nitrous acid is the reactant that carries out substrate oxidation, and systems that are not pre-loaded with nitrous acid will experience an induction period before significant reactivity commences. Under oxidizing fuel-lean conditions, nitrogen dioxide may induce autocatalytic nitrous acid formation, which may supplement additional side-reactions, potentially leading to thermal runaway. Therefore, the system is most vulnerable at the end of an induction period. Metal nitrate salts promote this ambient temperature solution chemistry (cf. traditional oxidizer properties of metal nitrate salts, which operate in the solid state at elevated temperatures) by releasing nitric acid into the system via hydrolysis. Unlike simple alcohols with a single hydroxyl functional group, the chemistry of polymeric carbohydrates (polysaccharides) such as starches and cellulose is more diverse. Therefore, the oxidation of the simple monomeric carbohydrate glucose is first examined to provide insight into the analogous reactivity with polysaccharides. Although less reactive than alcohols by virtue of extensive hydrogen bonding, carbohydrates are oxidized by nitric acid through the same fundamental mechanisms, complicated by a variety of competing side reactions including acid-catalyzed hydrolytic depolymerization to more reactive (ultimately monomeric) units, as well as other degradation reactions promoted by radical mechanisms. For this reason, polysaccharides such as starches that are more susceptible to hydrolysis are more reactive than cellulose, with the attendant microbial population in starch based kitty litter adding a special aspect of reactivity. From this safety evaluation, a bounding latency period has been developed beyond which a stored TRU waste drum that contains polysaccharides (starch based kitty litter excluded) commingled with nitric acid and/or metal nitrate salts may no longer present a hazard under ambient conditions, based on a compilation of induction periods and reaction times involving nitric acid with various oxidizable substrates under a variety of conditions from synthetic procedures, tests, and adverse incident scenarios. By obviating the need for costly treatment options or characterization, a better understanding of nitrate redox chemistry and the hazards it presents with polysaccharide oxidation thus provides a more efficient means to safely dispose of TRU waste generated within the Department of Energy (DOE) complex. (author)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
PEGylated DC-Chol/DOPE cationic liposomes containing KSP siRNA as a systemic siRNA delivery Carrier for ovarian cancer therapy
Although siRNA-mediated downregulation technology has been highly successful in suppressing the expression of any disease-related gene, systemic delivery of siRNA for the clinical applications remains challenging, especially in the use of cancer therapy. DC-Chol/DOPE cationic liposomes as one of the most attractive vehicles for gene delivery have been widely exploited for transfection of siRNA into cells, but complexity of systemic delivery has allowed only their direct injection into local targets due to the formation of aggregations with negatively-charged blood components. Herein, we demonstrate the effects of PEGylation on DC-Chol/DOPE cationic liposomes for systemic siRNA delivery in cancer therapy. In contrast to non-PEGylated DC-Chol/DOPE-siRNA lipoplexes, PEGylated DC-Chol/DOPE-siRNA lipoplexes reduce the excretion by kidneys and scavenging in liver, prolonging the circulation time in vivo, and ultimately increase their preferential tumor accumulation. Therefore, systemic injection of PEGylated DC-Chol/DOPE liposomes loaded with siRNA against kinesin spindle protein (KSP) gene exhibited a high level of target gene silencing at tumor sites and substantial suppression of tumor growth. Furthermore, systemically administered PEGylated lipoplexes did not lead to any activation of innate immune responses in the immunocompetent mice. These results suggest the potential of PEGylated DC-Chol/DOPE liposomes as a systemic delivery carrier for siRNA-mediated cancer therapy.
60 APPLIED LIFE SCIENCES↗
Synergistic Solvent-Surface Interactions Enable Alkyne Semihydrogenation at Palladium
Enabling higher yield and better selectivity for fine-chemical synthesis through heterogeneous catalysis is intricately linked to the interplay of active sites, reaction conditions, and mass transfer influence provided by the catalyst. Alkyne semihydrogenation is ubiquitous in the production of bulk chemicals in the pharmaceutical, polymer, or fine-chemical industries, but product selectivity remains a major challenge. Here, in this study, we demonstrate that the design of catalysts encompassing nickel (Ni) foams as contiguous monolith supports, decorated with ultralow loading of Pd/PdO x nanoparticles on a carbonized polydopamine interface and tuned with a thin layer of Al 2 O 3 , in conjunction with an optimized reaction environment leads to highly selective alkyne semihydrogenation. The reactions demonstrate good functional group tolerance and applicability to flow reactor systems. Combined computational and experimental studies are presented to describe the synergistic effect between the solvent-surface interaction and the degree of Pd surface reduction that are necessary to promote this selectivity. The system highlights the opportunity for catalyst-solvent codesign as a benign alternative to more complex reactants featuring extrinsic poisons or less-favored dopants.
atomic layer deposition↗
Scalable Deep Learning-Based Microarchitecture Simulation on GPUs
Cycle-accurate microarchitecture simulators are essential tools for designers to architect, estimate, optimize, and manufacture new processors that meet specific design expectations. However, conventional simulators based on discrete-event methods often require an exceedingly long time-to-solution for the simulation of applications and architectures at full complexity and scale. Given the excitement around wielding the machine learning (ML) hammer to tackle various architecture problems, there have been attempts to employ ML to perform architecture simulations, such as Ithemal and SimNet. However, the direct application of existing ML approaches to architecture simulation may be even slower due to overwhelming memory traffic and stringent sequential computation logic. This work proposes the first graphics processing unit (GPU)-based microarchitecture simulator that fully unleashes the potential of GPUs to accelerate state-of-the-art ML-based simulators. First, considering the application traces are loaded from central processing unit (CPU) to GPU for simulation, we introduce various designs to reduce the data movement cost between CPUs and GPUs. Second, we propose a parallel simulation paradigm that partitions the application trace into sub-traces to simulate them in parallel with rigorous error analysis and effective error correction mechanisms. Combined, this scalable GPU-based simulator outperforms by orders of magnitude the traditional CPU-based simulators and the state-of-the-art ML-based simulators, i.e., SimNet and Ithemal.
97 MATHEMATICS AND COMPUTING↗
BuildingIQ Technology Field Validation
This document describes a field validation and verification of the BuildingIQ Predictive Energy Optimization (PEO) technology based on a five-site study. BuildingIQ describes its PEO technology as a software-as-a-service (SaaS) platform that optimizes commercial building HVAC control for system efficiency, occupant comfort, and cost. It is targeted for use in large, complex buildings, and integrates with the building automation system (BAS) to conduct supervisory control. The PEO algorithm defines optimal space air temperature setpoints that are automatically implemented at the variable air volume (VAV) terminal units when possible, or through supply air temperature and duct static pressure setpoints at the air handling unit (AHU) level. The optimization is built upon a learned predictive model that provides a 24-hour ahead forecast of the building’s power profile, using weather forecasts and historical operational data; this model is updated every 4 to 6 hours. Demand-responsive load reductions may also be implemented.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
BuildingIQ Technology Field Validation
This document describes a field validation and verification of the BuildingIQ Predictive Energy Optimization (PEO) technology based on a five-site study. BuildingIQ describes its PEO technology as a software-as-a-service (SaaS) platform that optimizes commercial building HVAC control for system efficiency, occupant comfort, and cost. It is targeted for use in large, complex buildings, and integrates with the building automation system (BAS) to conduct supervisory control. The PEO algorithm defines optimal space air temperature setpoints that are automatically implemented at the variable air volume (VAV) terminal units when possible, or through supply air temperature and duct static pressure setpoints at the air handling unit (AHU) level. The optimization is built upon a learned predictive model that provides a 24-hour ahead forecast of the building’s power profile, using weather forecasts and historical operational data; this model is updated every 4 to 6 hours. Demand-responsive load reductions may also be implemented.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Evaluation of Structural Lattices for a Davis Gun Earth Penetrator Impact Experiment
The advanced materials team investigated the use of additively manufactured metallic lattice structures for mitigating impact response in a Davis gun earth penetrator impact experiment. High-fidelity finite element models were developed and validated with quasistatic experiments. These models were then used to simulate the response of such lattices when subjected to the acceleration loads expected in the Davis gun experiment. Results reveal how the impact mitigation performance of lattices can change drastically at a certain relative density. Based on these observations, an experiment deck was designed to probe the response of lattices with different relative densities during the Davis gun phase 2 shots. The expected performance of these lattices is predicted before testing based on simulation results. The results of the Davis gun phase 2 shots are expected to provide data which will be used to assess the predictive capability of the finite element simulations in such a complex impact environment.
36 MATERIALS SCIENCE↗
Consolidated Hydropower Data Repository: Value and Opportunities
Hydropower is one of several types of generating assets that provides energy, capacity, and services to electric power systems. It does so under rubrics and objectives—market driven and regulated, internal and external to asset and fleet owners—that address reliability, cost, price, and, increasingly, flexibility of output. The aggregation of data from multiple hydropower units can provide insights into asset operations and maintenance practices and needs and assist in meeting hydropower objectives. This paper examines the concept and potential benefits of aggregating hydropower asset data—primarily supervisory control and data acquisition (SCADA) information—with examples of insights developed from data aggregated by the Hydropower Research Institute (HRI). Data aggregation as discussed herein, and as implemented by the HRI, extends beyond multiple units in a powerhouse and beyond multiple hydropower facilities in an electric utility fleet or river system. Examples of research and analytics from such aggregated datasets range from unit load dependency analyses to modeling sensor measurements to detect and diagnose anomalies in assets. These examples showed the benefits of utilizing the entire dataset for insights into how the sensor layout of a single unit or set of units compares to the hydropower industry overall. Such insights include whether additional sensors are needed to complete analyses or to make decisions. In addition, utilizing multiple sensors of the same kind within a unit can provide an indication of possible current or upcoming problems with equipment. Although other analyses are possible, their use requires the development of complex models and, potentially, access to types of data that are currently not included with the example dataset used in this study. However, the examples studied herein confirmed the value of data aggregation in the fleet and unit contexts, and the value extends beyond multiple units in a powerhouse and beyond multiple hydropower facilities in an electric utility fleet or river system. The assessments also provided insights into potential extensions to the data aggregation concept that could further add to their value to the hydropower community, and these are included in this document as a set of recommendations.
13 HYDRO ENERGY↗
Large Area Detection of Microstructural Defects with Multi-Mode Ultrasonic Signals
Cyclic loading or other stresses can lead to development of cracks and crack growth in mechanical structures, leading to eventual failure. While ultrasound imaging can be used for non-destructive testing of such structures, conventional ultrasound techniques are often limited by crack size, density, and areal coverage. An effective characterization of real-world, large-area structures is required at an early damage stage to prevent catastrophic failure and predict remaining life. In this study, a new nonlinear ultrasonic testing (NUT) method is proposed for large-area monitoring of practical structures with arbitrary complexity by using multiple-mode guided-wave ultrasonic signals. The proposed guided-wave NUT technique requires single-element transducers, simple electronics, and a mixed time-frequency domain signal processing. As a proof-of-concept demonstration, numerical simulations and experiments are performed on an A36 carbon steel beam assembly with previously formed microstructural defects that cause nonlinearities in ultrasonic response. The quadratic dependence of the nonlinear wave excitation on the input ultrasonic signal amplitude is shown by numerical simulations, and such a nonlinear ultrasonic response is experimentally observed in the zone with a high density of microstructural defects.
36 MATERIALS SCIENCE↗
Polypropylene Composites Reinforced With Recycled Waste Cellulosic Fiber/Fine Mixture: The Impact of Cellulose Sieving on Performance
This study explores how a sieving step of waste cellulosic fiber and fine (WCFF) mixture affects the performance of WCFF‐loaded polypropylene (PP) composites and whether the separation of fines from fibers offers an added benefit. The WCFF samples were downsized, and four different filler size ranges were sieved using a series of mesh sizes from 4 to 0.85 mm. The WCFF/PP composites were then compounded at 20 wt.% loading of WCFF using a twin‐screw extruder. Incorporating WCFF increased the tensile strength to 41.28 MPa and the modulus to 3207 MPa, accounting for 28% and 38% enhancements, respectively. Interestingly, the greatest improvements were associated with the nonsieved WCFF case, and the sieved WCFF fibers provided only marginal enhancements over virgin PP. The outperformance of nonsieved WCFF was attributed to the synergistic reinforcement of hybrid fibers and fines as well as the maintenance of longer fibers in the system. However, the strain at break and impact strength of PP decreased after introducing WCFF. Moreover, the complex viscosity and storage modulus increased with an increase in the filler size, due to the formation of a more effective percolative network. The PP's crystallinity exhibited a relatively strong dependency on the sieving, where WCFF samples with short‐aspect‐ratio fillers promoted the crystallinity significantly. It was also found that the WCFF degradation onset temperature increased once it was incorporated into PP. This study suggests that waste cellulosic feedstocks can be utilized as a reinforcement without additional sieving to manufacture high‐performance and cost‐effective composites.
36 MATERIALS SCIENCE↗