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Abstract Plant cellulose microfibrils are increasingly employed to produce functional nanofibers and nanocrystals for biomaterials, but their catalytic formation and conversion mechanisms remain elusive. Here, we characterize length-reduced cellulose nanofibers assembly in situ accounting for the high density of amorphous cellulose regions in the natural rice fragile culm 16 ( Osfc16 ) mutant defective in cellulose biosynthesis using both classic and advanced atomic force microscopy (AFM) techniques equipped with a single-molecular recognition system. By employing individual types of cellulases, we observe efficient enzymatic catalysis modes in the mutant, due to amorphous and inner-broken cellulose chains elevated as breakpoints for initiating and completing cellulose hydrolyses into higher-yield fermentable sugars. Furthermore, effective chemical catalysis mode is examined in vitro for cellulose nanofibers conversion into nanocrystals with reduced dimensions. Our study addresses how plant cellulose substrates are digestible and convertible, revealing a strategy for precise engineering of cellulose substrates toward cost-effective biofuels and high-quality bioproducts. 

The FASER experiment is a new small and inexpensive experiment that is being placed 480 meters downstream of the ATLAS experiment at the CERN LHC. The experiment will shed light on currently unexplored phenomena, having the potential to make a revolutionary discovery. FASER is designed to capture decays of exotic particles, produced in the very forward region, out of the ATLAS detector acceptance. This talk will present the physics prospects, the detector design, and the construction progress of FASER. The experiment has been successfully installed and will take data during the LHC Run-3. 

Abstract This paper presents an adaptive Kriging based method to perform uncertainty quantification (UQ) of the photoelectron sheath and dust levitation on the lunar surface. The objective of this study is to identify the upper and lower bounds of the electric potential and that of dust levitation height, given the intervals of model parameters in the one-dimensional (1D) photoelectron sheath model. To improve the calculation efficiency, we employ the widely used adaptive Kriging method (AKM). A task-oriented learning function and a stopping criterion are developed to train the Kriging model and customize the AKM. Experiment analysis shows that the proposed AKM is both accurate and efficient. 

Uncertainty quantification (UQ) in metal additive manufacturing (AM) has attracted tremendous interest in order to dramatically improve product reliability. Model-based UQ, which relies on the validity of a computational model, has been widely explored as a potential substitute for the time-consuming and expensive UQ solely based on experiments. However, its adoption in the practical AM process requires overcoming two main challenges: (1) the inaccurate knowledge of uncertainty sources and (2) the intrinsic uncertainty associated with the computational model. Here, we propose a data-driven framework to tackle these two challenges by combining high throughput physical/surrogate model simulations and the AM-Bench experimental data from the National Institute of Standards and Technology (NIST). We first construct a surrogate model, based on high throughput physical simulations, for predicting the three-dimensional (3D) melt pool geometry and its uncertainty with respect to AM parameters and uncertainty sources. We then employ a sequential Bayesian calibration method to perform experimental parameter calibration and model correction to significantly improve the validity of the 3D melt pool surrogate model. The application of the calibrated melt pool model to UQ of the porosity level, an important quality factor, of AM parts, demonstrates its potential use in AM quality control. The proposed UQ framework can be generally applicable to different AM processes, representing a significant advance toward physics-based quality control of AM products.