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We review the effective field theory (EFT) bootstrap by formulating it as an infinite-dimensional semidefinite program (SDP), built from the crossing symmetric sum rules and the S-matrix primal ansatz. We apply the program to study the large-Nchiral perturbation theory (χPT) and observe excellent convergence of EFT bounds between the dual (rule-out) and primal (rule-in) methods. This convergence aligns with the predictions of duality theory in SDP, enabling us to analyze the bound states and resonances in the ultra-violet (UV) spectrum. Furthermore, we incorporate the upper bound of unitarity to uniformly constrain the EFT space from the UV scaleMusing the primal method, thereby confirming the consistency of the large-Nexpansion. In the end, we translate the large-N χPT bounds to constrain the higher derivative corrections of holographic QCD models.

 

Reinforcement corrosion has been recognized as an influential factor in the seismic fragility, both demand and capacity models, of aging reinforced concrete (RC) bridges. For capacity models, accurate and applied prediction tools accounting for aging effects are yet to be well established. Current practices usually perform numerical analyses to obtain time-variant capacity models, which are time-consuming particularly when multi-source structural and environmental uncertainties are considered and sometimes even suffer computational non-convergence. To address these issues, this study leverages a rigorously optimized artificial neural network architecture to develop data-driven models for rapid estimates of probabilistic curvature capacity of corroded circular RC bridge columns with flexural failure modes. An extensive database of multi-level curvature limit states (i.e. slight, moderate, severe, and complete) is created through experimentally validated moment–curvature analyses. A new threshold for the moderate limit state is defined based on the strain of core concrete, rather than cover concrete, to account for the potential full erosion of the cover with drastic corrosion. The data-driven probabilistic capacity models are applied to aid the lifetime seismic fragility assessment of a typical highway bridge, where the spectral acceleration at 1.0 s (Sa-10), peak ground velocity (PGV), and Housner intensity (HI) are found consistently, for the first time, as optimal intensity measures for probabilistic demand modeling of RC columns with different extents of aging effects. For the ease of application, the database and code for the data-driven probabilistic capacity models are accessible at https://bit.ly/3uAa8EY .

 

2D van der Waals (vdW) magnets open landmark horizons in the development of innovative spintronic device architectures. However, their fabrication with large scale poses challenges due to high synthesis temperatures (>500 °C) and difficulties in integrating them with standard complementary metal‐oxide semiconductor (CMOS) technology on amorphous substrates such as silicon oxide (SiO₂) and silicon nitride (SiN_x). Here, a seeded growth technique for crystallizing CrTe₂films on amorphous SiN_x/Si and SiO₂/Si substrates with a low thermal budget is presented. This fabrication process optimizes large‐scale, granular atomic layers on amorphous substrates, yielding a substantial coercivity of 11.5 kilo‐oersted, attributed to weak intergranular exchange coupling. Field‐driven Néel‐type stripe domain dynamics explain the amplified coercivity. Moreover, the granular CrTe₂devices on Si wafers display significantly enhanced magnetoresistance, more than doubling that of single‐crystalline counterparts. Current‐assisted magnetization switching, enabled by a substantial spin–orbit torque with a large spin Hall angle (85) and spin Hall conductivity (1.02 ×  10⁷ℏ/2e  Ω⁻¹  m⁻¹), is also demonstrated. These observations underscore the proficiency in manipulating crystallinity within integrated 2D magnetic films on Si wafers, paving the way for large‐scale batch manufacturing of practical magnetoelectronic and spintronic devices, heralding a new era of technological innovation.

 

High entropy alloy (HEA) nanoparticles hold promise as active and durable (electro)catalysts. Understanding their formation mechanism will enable rational control over composition and atomic arrangement of multimetallic catalytic surface sites to maximize their activity. While prior reports have attributed HEA nanoparticle formation to nucleation and growth, there is a dearth of detailed mechanistic investigations. Here we utilize liquid phase transmission electron microscopy (LPTEM), systematic synthesis, and mass spectrometry (MS) to demonstrate that HEA nanoparticles form by aggregation of metal cluster intermediates. AuAgCuPtPd HEA nanoparticles are synthesized by aqueous co-reduction of metal salts with sodium borohydride in the presence of thiolated polymer ligands. Varying the metal : ligand ratio during synthesis showed that alloyed HEA nanoparticles formed only above a threshold ligand concentration. Interestingly, stable single metal atoms and sub-nanometer clusters are observed by TEM and MS in the final HEA nanoparticle solution, suggesting nucleation and growth is not the dominant mechanism. Increasing supersaturation ratio increased particle size, which together with observations of stable single metal atoms and clusters, supported an aggregative growth mechanism. Direct real-time observation with LPTEM imaging showed aggregation of HEA nanoparticles during synthesis. Quantitative analyses of the nanoparticle growth kinetics and particle size distribution from LPTEM movies were consistent with a theoretical model for aggregative growth. Taken together, these results are consistent with a reaction mechanism involving rapid reduction of metal ions into sub-nanometer clusters followed by cluster aggregation driven by borohydride ion induced thiol ligand desorption. This work demonstrates the importance of cluster species as potential synthetic handles for rational control over HEA nanoparticle atomic structure.