Deep learning-powered efficient characterization and quantification of microplastics.

Characterizing and quantifying microplastics (MPs) are time-consuming and labor-intensive tasks traditionally. This paper presents an artificial intelligence (AI) framework aiming to automate these tasks by integrating computer vision and deep learning techniques. The approach leverages Fourier Transform Infrared (FTIR) spectra and visual images. Primary novelties of this research involve the development of: (1) an AI framework integrating efforts of data processing, analytics, visualization, and human-computer interaction; (2) a method for transforming FTIR data into contour images; (3) data augmentation strategies for resolving data scarcity and imbalance issues; (4) deep learning models for identifying MPs; (5) computer vision algorithms for quantifying MPs; and (6) an engineer-friendly graphic user interface (GUI) for enhancing data accessibility. The AI framework has been applied to polyethylene, polypropylene, polystyrene, polyamide, ethylene-vinyl acetate, and cellulose acetate. Results confirmed the efficacy of the framework, exhibiting high accuracy scores in classification (98 %), segmentation (99 %), and quantification (96 %) tasks. This research advances the capability of automatic assessment of MPs.

Lightweight Calcium-Silicate-Hydrate Nacre with High Strength and High Toughness.

Low flexural strength and toughness have posed enduring challenges to cementitious materials. As the main hydration product of cement, calcium silicate hydrate (C-S-H) plays important roles in the mechanical performance of cementitious materials while exhibiting random microstructures with pores and defects, which hinder mechanical enhancement. Inspired by the "brick-and-mortar" microstructure of natural nacre, this paper presents a method combining freeze casting, freeze-drying, in situ polymerization, and hot pressing to fabricate C-S-H nacre with high flexural strength, high toughness, and lightweight. Poly(acrylamide-co-acrylic acid) was used to disperse C-S-H and toughen C-S-H building blocks, which function as "bricks", while poly(methyl methacrylate) was impregnated as "mortar". The flexural strength, toughness, and density of C-S-H nacre reached 124 MPa, 5173 kJ/m3, and 0.98 g/cm3, respectively. The flexural strength and toughness of the C-S-H nacre are 18 and 1230 times higher than those of cement paste, respectively, with a 60% reduction in density, outperforming existing cementitious materials and natural nacre. This research establishes the relationship between material composition, fabrication process, microstructure, and mechanical performance, facilitating the design of high-performance C-S-H-based and cement-based composites for scalable engineering applications.

Artificial intelligence-empowered collection and characterization of microplastics: A review.

Microplastics have been detected from water and soil systems extensively, with increasing evidence indicating their detrimental impacts on human and animal health. Concerns surrounding microplastic pollution have spurred the development of advanced collection and characterization methods for studying the size, abundance, distribution, chemical composition, and environmental impacts. This paper offers a comprehensive review of artificial intelligence (AI)-empowered technologies for the collection and characterization of microplastics. A framework is presented to streamline efforts in utilizing emerging robotics and machine learning technologies for collecting, processing, and characterizing microplastics. The review encompasses a range of AI technologies, delineating their principles, strengths, limitations, representative applications, and technology readiness levels, facilitating the selection of suitable AI technologies for mitigating microplastic pollution. New opportunities for future research and development on integrating robots and machine learning technologies are discussed to facilitate future efforts for mitigating microplastic pollution and advancing AI technologies.

Valorization of Vetiver Root Biochar in Eco-Friendly Reinforced Concrete: Mechanical, Economic, and Environmental Performance.

Biochar has shown great promise in producing low-cost low-carbon concrete for civil infrastructure applications. However, there is limited research comparing the use of pristine and contaminated biochar in concrete. This paper presents comprehensive laboratory experiments and three-dimensional nonlinear finite element analysis on the mechanical, economical, and environmental performance of reinforced concrete beams made using concrete blended with biochar generated from vetiver grass roots after the roots were used in an oil extraction process. Both pristine biochar and biochar that were used to treat wastewater through adsorbing heavy metals (100 mg/L of Pb, Cu, Cd, and Zn) were investigated. The biochar was used to replace up to 6% Portland cement in concrete. Laboratory experiments were conducted to characterize the workability, mechanical properties, shrinkage, and leaching potential of the concrete blended with biochar. The results showed that using biochar could increase the compressive strengths and reduce the shrinkage of concrete without causing a leaching problem. The results from finite element analysis of the reinforced concrete beams showed that the use of biochar was able to increase the flexural performance of the beams as well as their economic and environmental performance. This research will promote the development and structural applications of low-cost low-carbon concrete.

Arsenate removal using titanium dioxide-doped cementitious composites: Mixture design, mechanisms, and simulated sewer application.

Arsenate (As(V)) in municipal wastewater leads to a public health problem due to its contamination of natural water sources. Here, we proposed to use sewer pipe made of TiO2-doped cementitious composite (TCC) for As(V) removal from municipal wastewater. The optimum composition of TCC, the performance for As(V) removal in the simulated sewer system, and the molecular-level As(V) removal mechanisms were investigated. To obtain the optimum composition, variables were adjusted to maximize the As(V) removal using TCC. Results show that the TiO2 and water contents were the dominant factors. Simulated sewer pipes made of TCC removed As(V) from 100 µg/L to <10 µg/L, which performed better than plain cementitious composite. Moreover, extended X-ray absorption fine structure (EXAFS) analysis indicates that both precipitation and adsorption contribute to the As(V) removal by TCC, while the adsorption is more significant with a lower As(V) concentration (i.e., 1 mg/L). This is the first study evaluating the feasibility to apply TCC for As(V) removal from sewer wastewater. The optimized composition, simulation results, and molecular-level mechanism gained from this study are useful to the future design of TCC for As(V) removal, especially for sewer systems.

Predicting Mechanical Properties of High-Performance Fiber-Reinforced Cementitious Composites by Integrating Micromechanics and Machine Learning.

Current development of high-performance fiber-reinforced cementitious composites (HPFRCC) mainly relies on intensive experiments. The main purpose of this study is to develop a machine learning method for effective and efficient discovery and development of HPFRCC. Specifically, this research develops machine learning models to predict the mechanical properties of HPFRCC through innovative incorporation of micromechanics, aiming to increase the prediction accuracy and generalization performance by enriching and improving the datasets through data cleaning, principal component analysis (PCA), and K-fold cross-validation. This study considers a total of 14 different mix design variables and predicts the ductility of HPFRCC for the first time, in addition to the compressive and tensile strengths. Different types of machine learning methods are investigated and compared, including artificial neural network (ANN), support vector regression (SVR), classification and regression tree (CART), and extreme gradient boosting tree (XGBoost). The results show that the developed machine learning models can reasonably predict the concerned mechanical properties and can be applied to perform parametric studies for the effects of different mix design variables on the mechanical properties. This study is expected to greatly promote efficient discovery and development of HPFRCC.

NAD-biosynthetic enzyme NMNAT1 reduces early behavioral impairment in the htau mouse model of tauopathy.

NAD metabolism and the NAD biosynthetic enzymes nicotinamide nucleotide adenylyltransferases (NMNATs) are thought to play a key neuroprotective role in tauopathies, including Alzheimer's disease. Here, we investigated whether modulating the expression of the NMNAT nuclear isoform NMNAT1, which is important for neuronal maintenance, influences the development of behavioral and neuropathological abnormalities in htau mice, which express non-mutant human tau isoforms and represent a model of tauopathy relevant to Alzheimer's disease. Prior to the development of cognitive symptoms, htau mice exhibit tau hyperphosphorylation associated with a selective deficit in food burrowing, a behavior reminiscent to activities of daily living which are impaired early in Alzheimer's disease. We crossed htau mice with Nmnat1 transgenic and knockout mice and tested the resulting offspring until the age of 6 months. We show that overexpression of NMNAT1 ameliorates the early deficit in food burrowing characteristic of htau mice. At 6 months of age, htau mice did not show neurodegenerative changes in both the cortex and hippocampus, and these were not induced by downregulating NMNAT1 levels. Modulating NMNAT1 levels produced a corresponding effect on NMNAT enzymatic activity but did not alter NAD levels in htau mice. Although changes in local NAD levels and subsequent modulation of NAD-dependent enzymes cannot be ruled out, this suggests that the effects seen on behavior may be due to changes in tau phosphorylation. Our results suggest that increasing NMNAT1 levels can slow the progression of symptoms and neuropathological features of tauopathy, but the underlying mechanisms remain to be established.

Penetration and intracellular uptake of poly(glycerol-adipate) nanoparticles into three-dimensional brain tumour cell culture models.

Nanoparticle (NP) drug delivery systems may potentially enhance the efficacy of therapeutic agents. It is difficult to characterize many important properties of NPs in vivo and therefore attempts have been made to use realistic in vitro multicellular spheroids instead. In this paper, we have evaluated poly(glycerol-adipate) (PGA) NPs as a potential drug carrier for local brain cancer therapy. Various three-dimensional (3-D) cell culture models have been used to investigate the delivery properties of PGA NPs. Tumour cells in 3-D culture showed a much higher level of endocytic uptake of NPs than a mixed normal neonatal brain cell population. Differences in endocytic uptake of NPs in 2-D and 3-D models strongly suggest that it is very important to use in vitro 3-D cell culture models for evaluating this parameter. Tumour penetration of NPs is another important parameter which could be studied in 3-D cell models. The penetration of PGA NPs through 3-D cell culture varied between models, which will therefore require further study to develop useful and realistic in vitro models. Further use of 3-D cell culture models will be of benefit in the future development of new drug delivery systems, particularly for brain cancers which are more difficult to study in vivo.