In order to explore the feasibility and efficacy of reed-fiber-modified bitumen (RFMB), three lengths and three dosages of reed fibers were selected to modify bitumen and bituminous mortar, while the physicochemical properties of RFMB and RFMB mortar were analyzed. In this work, FTIR spectroscopy was employed to characterize the chemical impact of fiber on bitumen. The viscidity and rheology of RFMB and the tensile strength of RFMB mortar were evaluated using a Brookfield viscometer, dynamic shear rheometer, and monotonic tensile test. The results showed that adding fibers primarily affects the physical structure rather than the chemical composition of bitumen, confirmed by FTIR spectroscopy. RFMB viscosity increased with higher fiber dosage and fiber length. Rheological evaluations showed an enhanced complex shear modulus for RFMB, suggesting improved performance at higher temperatures but increased stiffness at lower temperatures, with the latter indicating reduced flexibility. RFMB also demonstrated superior fatigue and rutting resistance, albeit with compromised stress sensitivity. Tensile tests on RFMB mortar highlighted significant improvements, especially with longer fibers, while shorter 0.4 mm fibers showed modest reinforcement effects, possibly due to uneven distribution during sample preparation.@en

This study enhances the molecular analysis of bitumen by transitioning from traditional chemical descriptors, such as SARA (Saturates, Aromatics, Resins, and Asphaltenes) fractions and elemental compositions, to specific force field atom types in Molecular Dynamics (MD) models. This shift improves the precision in predicting material properties critical for bituminous material characterization. Machine Learning Models (MLMs) were developed to use these atom types as input features, inherently reflecting fundamental chemical characteristics. Trained on data from over 1,770 LAMMPS simulations of diverse bitumen types and conditions, these MLMs enable the prediction of properties like density, heat capacity, solubility parameters, and thermal expansion coefficients without the need for additional MD simulations. The models utilize 30 chemical descriptors corresponding to specific atom types in the PCFF force field, which collectively account for over 95% of the influence on these properties. By accurately predicting fundamental, thermodynamic, and kinetic properties, the use of MLMs and force field atom types allows researchers to efficiently tweak the chemical nature of organic molecules and mixtures to achieve desired properties. With near-instantaneous prediction times, these MLMs offer valuable insights for advancing bitumen research in the construction and petroleum industries, reducing the need for more intensive simulation techniques.@en

In the ‘Beyond Moore’s Law’ era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same time, adopting a variety of nanotechnologies will offer benefits in energy cost, computational speed, reduced footprint, cyber resilience, and processing power. The time is ripe for a roadmap for unconventional computing with nanotechnologies to guide future research, and this collection aims to fill that need. The authors provide a comprehensive roadmap for neuromorphic computing using electron spins, memristive devices, two-dimensional nanomaterials, nanomagnets, and various dynamical systems. They also address other paradigms such as Ising machines, Bayesian inference engines, probabilistic computing with p-bits, processing in memory, quantum memories and algorithms, computing with skyrmions and spin waves, and brain-inspired computing for incremental learning and problem-solving in severely resource-constrained environments. These approaches have advantages over traditional Boolean computing based on von Neumann architecture. As the computational requirements for artificial intelligence grow 50 times faster than Moore’s Law for electronics, more unconventional approaches to computing and signal processing will appear on the horizon, and this roadmap will help identify future needs and challenges. In a very fertile field, experts in the field aim to present some of the dominant and most promising technologies for unconventional computing that will be around for some time to come. Within a holistic approach, the goal is to provide pathways for solidifying the field and guiding future impactful discoveries.

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Pdb2dat, developed in Python, is an open-source, self-contained utility that facilitates the conversion of PDB files into LAMMPS data files, catering to the need of initializing atomistic simulation from initial atomic configurations. It extracts molecular details from PDB files, uses Rdkit and Xyz2mol for bonding analysis and 3D conformer generation, and uses Pysimm for assigning force field types and charges. Designed to be lightweight and fully Pythonic, pdb2dat is suitable for use in privilege-limited high-throughput environments. The output details system topologies for use in MD simulations, significantly simplifying the preparatory steps needed by researchers to explore materials phenomena through LAMMPS.@en

To meet the high temperature and anti-reveling properties required in open-graded friction course, high content polymer modified asphalt (HCPMA) is gradually widely used in China, but the rheological, chemical and aging characteristic is not clear yet. In this paper, HCPMA with different SBS content and different base asphalt are prepared, and Pressure Aging Vessel (PAV) aging was conducted to simulate the long-term aging condition. The chemical and rheological evaluation of HCPMA before and after aging were tracked with Fourier transform infrared, gel permeation chromatography test, dynamic shear oscillatory test, master curve and multiple stress creep and recovery test. The results show that firstly, the aging of HCPMA is combined with hardening of asphalt and degradation of Styrene–butadiene–styrene (SBS) polymer. Furthermore, the addition of high content of SBS polymer can reduce the formation of carbonyl, but the degradation rate of SBS polymer is not related to the content of SBS or the type of base asphalt. Besides, HCPMA with a higher SBS content will have better rheological properties, but in consideration of economy, 9% is optimum dosage. At last, HCPMA prepared with Esso asphalt as base binder exhibits better rheological properties than HCPMA prepared with SK asphalt. However, the rheology difference reduces with the increase of SBS content and after PAV aging.

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