Xiaonan Wang

Tsinghua University | TH · Department of Chemical Engineering

p>In materials science, the discovery of recipes that yield nanomaterials with defined optical properties is costly and time-consuming. In this study, we present a two-step framework for a machine learning driven high-throughput microfluidic platform to rapidly produce silver nanoparticles with a desired absorbance spectrum. Combining a Gaussian Pr...

Carbon neutrality by 2060 is the recent expression of China’s international commitment to reduce its carbon dioxide emissions. Energy and chemical sectors, the two main contributors for carbon dioxide emissions in China, are the biggest bottlenecks for reaching the objective of carbon neutrality. Moreover, coal-to-ammonia and coal-to-methanol are t...

Scanning probe microscopy (SPM) is recognized as an essential characterization tool in a broad range of applications, allowing for real-space atomic imaging of solid surfaces, nanomaterials, and molecular systems. Recently, the imaging of chiral molecular nanostructures via SPM has become a matter of increased scientific and technological interest...

Emerging soft machines require high-performance strain sensors to achieve closed-loop feedback control. Machine learning is a versatile tool to uncover complex correlations between fabrication recipes and sensor performance at the device level. Here a three-stage machine learning framework was realized for a high-accuracy prediction model capable o...

Global greenhouse gas (GHG) emissions from food loss and waste (FLW) are not well characterized from cradle to grave. Here GHG emissions due to FLW in supply chain and waste management systems are quantified, followed by an assessment of the GHG emission reductions that could be achieved by policy and technological interventions. Global FLW emitted...

The contradiction between the importance of materials to modern society and their slow development process has led to the development of multiple methods to accelerate materials discovery. The recently emerged concept of intelligent laboratories integrates the developments in fields of high-throughput experimentation, automation, theoretical comput...

As materials researchers increasingly embrace machine-learning (ML) methods, it is natural to wonder what lessons can be learned from other fields undergoing similar developments. In this Review, we comparatively assess the evolution of applied ML in materials research, gameplaying and robotics. We observe ML being integrated into each field in thr...

Designing polymeric membranes with high solute-solute selectivity and permeance is important but technically challenging. Existing industrial interfacial polymerization (IP) process to fabricate polyamide-based polymeric membranes is largely empirical, which requires enormous trial-and-error experimentations to identify optimal fabrication conditio...

Research on the relationship between a country’s renewable energy consumption and carbon emissions is of great significance for reducing carbon emissions embodied in international trade. There always exists a gap between production-based and consumption-based carbon emissions. Accordingly, this paper investigates the influence of renewable energy c...

Membrane technologies are becoming increasingly versatile and helpful today for sustainable development. Machine Learning (ML), an essential branch of artificial intelligence (AI), has substantially impacted the research and development norm of new materials for energy and environment. This review provides an overview and perspectives on ML methodo...

Interest in low emissions hydrogen as an energy vector to assist in deep decarbonization goals has gained momentum recently. In this paper, we explore local hydrogen production from natural gas with CO2 capture and sequestration (known as “blue” H2) to support Singapore's intended inclusion of low-carbon intensity H2 fuel as a way to achieve signif...

Rapid and accurate chemical composition identification is critically important in chemistry. While it can be achieved with optical absorption spectrometry by comparing the experimental spectra with the reference data when the chemical compositions are simple, such application is limited in more complicated scenarios especially in nano-scale researc...

Reactive composting is a promising technology for recovering valuable resources from food waste, while its manual regulation is laborious and time-consuming. In this study, machine learning (ML) technologies are adopted to enable automated composting by predicting compost maturity and providing process regulation. Four machine learning algorithms,...

Machine learning has been regarded as a promising method to better model thermochemical processes such as gasification. However, their black box nature can limit how much one can trust and learn from the developed models. Here seven different machine learning methods have been adopted to model the gasification of biomass and waste across a wide ran...

Wearable strain sensors that detect joint/muscle strain changes become prevalent at human–machine interfaces for full-body motion monitoring. However, most wearable devices cannot offer customizable opportunities to match the sensor characteristics with specific deformation ranges of joints/muscles, resulting in suboptimal performance. Adequate wea...

As an important subtopic within phytoremediation, hyperaccumulators have garnered significant attention due to their ability of super-enriching heavy metals. Identifying the factors that affecting phytoextraction efficiency has important application value in guiding the efficient remediation of heavy metal contaminated soil. However, it is challeng...

To meet the ever-increasing energy storage demands, there is an urgent need for developing next-generation batteries with high energy densities from an eco-friendly and sustainable resource. Vanadium metal-organic frameworks (V-MOFs) are regarded as important electrode materials for aqueous Zn-ion batteries (ZIBs) due to their large specific surfac...

Modeling is essential for designing, scaling up, controlling, and optimizing a reactor or process involving reactions. However, developing high-fidelity mechanistic models from first principles for reactor systems involving complex physiochemical phenomena is usually time- and resource-consuming. Therefore, machine learning models using data-driven...

Density functional theory (DFT) is a widely adopted methodology that gives quantum-level understanding of matter and guides materials discovery. In parallel, artificial intelligence (AI) for materials is an emerging interdisciplinary research direction whose major purpose is to accelerate the process of materials discovery. However, the shortage of...

Water hyacinth gasification, which generates syngas and biochar, is a promising thermochemical approach for bioenergy production and greenhouse gas mitigation. We investigated the economic feasibility, life-cycle greenhouse gas emission and human toxicity impact of two different water hyacinth gasification approaches: water hyacinth with and withou...

The design of low-carbon energy systems towards sustainable cities requires correctly quantifying uncertainties in future climate variations, since they affect both energy demand of urban cities and energy system performance. However, quantifying climate uncertainties is challenging due to the stochastic and unpredictable nature of future climate e...

The conversion of wet waste (e.g., food waste, sewage sludge, and animal manure) into bioenergy is a promising strategy for sustainable energy generation and waste management. Although experimental efforts have driven waste conversion technologies (WCT) to various degrees of maturity, computational modeling has equally contributed to this endeavor....

The multi-generation system often operates under off-design conditions due to the highly dynamic energy demand. Improving its off-design performance is crucial for energy conservation and emission reductions. To this end, this paper proposed a novel control strategy for the gas turbine cycle applied in combined heating and power (CHP) system, which...

Growing national decarbonization commitments require rapid and deep reductions of carbon dioxide emissions from existing fossil-fuel power plants. Although retrofitting existing plants with carbon capture and storage or biomass has been discussed extensively, yet such options have failed to provide evident emission reductions at a global scale so f...

This work considers a seeded fesoterodine fumarate (FF) cooling crystallization and presents the methodology and implementation of a real‐time machine learning modeling‐based predictive controller to handle batch‐to‐batch (B2B) parametric drift. Specifically, an autoencoder recurrent neural network‐based model predictive controller (AERNN‐MPC) is d...

In a conventional fuel vehicle, it is always difficult to find design points for a thermoelectric generator (TEG) because of the variation in engine power seen in different driving cycles. In contrast, the engine of an extended-range electric vehicle (EEV) is decoupled from the road load and only works at a single operating point in most cases, mak...

Due to rapid economic development and urbanisation, emerging megacities with dense populations have witnessed a significant increase in waste generation. Megacities face challenges in developing sustainable waste management systems. Considerable heterogeneity exists across megacities in management strategies. The two selected emerging megacities, S...

Gasification is a sustainable approach for biomass waste treatment with simultaneous combustible H2-syngas production. However, this thermochemical process was quite complicated with multi-phase products generated. The product distribution and composition also highly depend on the feedstock information and gasification condition. At present, it is...

In the past decades, H2 has attracted significant attention as a potentially low, zero, or negative-emissions fuel depending on how it is produced. However, how H2 will evolve in terms of its production, demand, and transport is not very clear. To help fill this gap, we developed a Python-based tool called the Hydrogen Economy Assessment & Resource...

Carbon capture technologies have been extensively investigated as indispensable tools for reducing CO2 emissions. In particular, CO2 capture using solid waste-derived porous carbons (SWDPCs) has attracted significant research attention as one of the most promising and sustainable approaches to simultaneously mitigate climate change and address soli...

Energy storage systems (ESS) are becoming more prevalent and indispensable in modern electrical infrastructure. The process of choosing the proper type of ESS technology for the application is the first step of designing ESS for optimal performance. However, the selection process involves a variety of factors, and currently there lacks a sophistica...

Industry 4.0 has ushered in a new era of connectivity and communication within various industries. Motivated by the United Nation’s Sustainable Development Goals (SDGs) of achieving net zero emissions by 2050, long-term energy sustainability plans encourage decentralized/distributed technologies such as blockchain to take center stage, alongside In...

Digitalisation enhances communication and therefore offers new ways to achieve efficiency gains in science, technology and society at large. However, there are still many open questions around how digitalisation can contribute to a more sustainable environment and lifestyle. We believe that knowledge graph technology is a promising candidate with w...

Mixed matrix membranes (MMMs) based on metal organic frameworks (MOFs) have been extensively studied for carbon capture to combat global warming. Here we report the introduction of machine learning to get more insights. Random forest models are first trained by literature data on CO2/CH4 separation, which reveal the optimum MOF structure with pore...

This work considers a seeded fesoterodine fumarate (FF) cooling crystallization and presents the methodology and implementation of a real-time machine learning modeling-based predictive controller to handle batch-to-batch (B2B) parametric drift. Specifically, an autoencoder recurrent neural network-based model predictive controller (AERNN-MPC) is d...

This communication paper provides an overview of multi-scale smart systems engineering (SSE) approaches and their applications in crucial domains including materials discovery, intelligent manufacturing, and environmental management. A major focus of this interdisciplinary field is on the design, operation and management of multi-scale systems with...

Biochar application is a promising strategy for the remediation of contaminated soil, while ensuring sustainable waste management. Biochar remediation of heavy metal (HM)-contaminated soil primarily depends on the properties of the soil, biochar, and HM. The optimum conditions for HM immobilization in biochar-amended soils are site-specific and var...

Islands are constrained by geographical conditions in terms of energy delivery. Due to weak connections with the mainland and the power grid, the diversity of island energy demand leads to high economic costs and environmental pollution issues. This study proposes a 100% renewable island energy system, which integrates with power-to-gas, combined c...

Conversion of oil palm mesocarp fibers (MF) into fermentable sugar through catalytic pretreatment coupled with enzymatic hydrolysis is a promising solution for biomass valorization. However, lack of understanding of the complicated conversion process and optimization of critical reaction conditions limits the efficacy of the sugar production (SP)....

For practical applications, molecules often exist in an aggregate state. Therefore, it is of great value if one can predict the performance of molecules when forming aggregates, for example, aggregation‐induced emission (AIE) or aggregation‐caused quenching (ACQ). Herein, a database containing AIE/ACQ molecules reported in the literature is first e...

The propagation of distributed renewable energy resources poses several challenges in the operation of microgrids due to uncertainty. In traditional energy scheduling approaches, the scheduling algorithm often depends on accurate forecasts of the uncertainties, which in many cases add complexities to the problem. While several data-driven algorithm...

The ever-increasing rise in the global population coupled with rapid urbanization demands considerable consumption of fossil fuel, food, and water. This in turn leads to energy depletion, greenhouse gas emissions and wet wastes generation (including food waste, animal manure, and sewage sludge). Conversion of the wet wastes to bioenergy and biochar...

Within the Industry 4.0 context, platforms such as cyber-physical production system (CPPS) offer numerous opportunities for smart energy management in manufacturing. In this study, we demonstrate the application of big data and machine learning (ML) to foster such practices for real manufacturing environments by taking the Model Factory (MF) in Sin...

After the onset of the COVID-19 pandemic, World Health Organization (WHO) launched COVAX in April 2020 to bring together countries and vaccine manufacturers and provide innovative and equitable access to COVID-19 vaccines. We developed a global supply chain model to optimize the production of the vaccines and allocation to different countries world...

Energy consumption can be a great environmental burden with heavy greenhouse gas emissions. Renewable energy, negative emission technologies, and waste-to-energy technologies are promising methods to assist in transitioning the energy- and carbon-intensive current energy systems towards low-carbon systems, mitigating emissions, contributing to carb...

This paper focuses on evaluating the performances of various approaches to solve a day-ahead energy scheduling Mixed Integer Linear Programming (MILP) cost minimization problem, with hybrid energy generation system consisting of solar photo voltaic (PV), waste to energy (WTE) and main grid. The system under consideration is a highly energy-intensiv...

Recent advances in machine learning (ML) have witnessed a profound interest and application in the domain of waste to energy. However, their black-box nature renders challenges for ubiquitous acceptance. To address this issue, we developed a novel and first-of-its-kind hybrid data-driven and mechanistic modelling approach for hydrothermal gasificat...

In materials science, the discovery of recipes that yield nanomaterials with defined optical properties is costly and time-consuming. In this study, we present a two-step framework for a machine learning-driven high-throughput microfluidic platform to rapidly produce silver nanoparticles with the desired absorbance spectrum. Combining a Gaussian pr...

Urban energy systems often contain multiple optimization sub-objectives with different dimensions. They often need to be normalized in many multi-objective optimization problems. This process may result in the loss of trade-off characteristics of each sub-objectives. This paper introduces a novel indicator, relative optimization potential, to quant...

As the COVID-19 continues to disrupt the global norms, there is the requirement of modelling frameworks to accurately assess and quantify the impact of the pandemic on the electricity sector and its emissions. In this study, we devise machine learning models to estimate the pandemic induced reduction in electricity consumption based on weather, eco...

Realizing general inverse design could greatly accelerate the discovery of new materials with user-defined properties. However, state-of-the-art generative models tend to be limited to a specific composition or crystal structure. Herein, we present a framework capable of general inverse design (not limited to a given set of elements or crystal stru...

Pyrolysis of the middle layer of a surgical mask (MLM) and inner and outer layers of a surgical mask (IOM) was performed to assess their potential valorization as waste-to-energy feedstocks, and the characteristics of the resulting products were investigated. Pyrolysis of the main organics in waste surgical masks occurred at a very narrow temperatu...

Hydrothermal liquefaction (HTL) of biomass with high moisture (e.g., algae, sludge, manure, and food waste) is a promising and sustainable approach to produce renewable energy (bio-oil) and protect the environment. However, the production of bio-oil with high yield and preferable properties such as low nitrogen content (N_oil) is time/labor-consumi...

A novel cycle model of the capacitive salinity/heat engine mainly consisting of nano-porous super-capacitors is established for harvesting mixed free energy caused by salinity difference between the river water and the seawater, and the thermal energy due to the temperature difference. The heat engine is charged and discharged in the cycle of a low...

Power plant heat rate is a plant level performance parameter that indicates the economy of power production, equipment’s safety, and availability. In this paper, seven operating parameters, including the performance indices of integrated energy devices and the environmental conditions are incorporated for modeling the power plant heat rate by Artif...

Soil and crop management are facing challenges, in particular an increasing pressure to feed a growing population and improve food safety. Smart farming assists in dealing with these issues by incorporating information and communication technologies into farming practice. Multiponics Vertical Farming (MVF) system is a very promising concept in smar...

Anaerobic digestion (AD) has been identified as an efficient food waste disposal technology by many researchers. However, a holistic environmental investigation of different AD configurations integrated with different downstream biogas utilization has never been reported. This study, taking Singapore as an example, compared the sustainability of bu...

Organic molecular fluorophores in the second near-infrared window (NIR-II) have attracted much attention in the recent decade due to their great potentials in both fundamental research and practical applications. This is especially true for biomedical research, owing to their deep light penetration depth and low bioluminescence background at the lo...

Organic molecular fluorophores in the second near-infrared window (NIR-II) have attracted much attention in the recent decade due to their great potentials in both fundamental research and practical applications. This is especially true for biomedical research, owing to their deep light penetration depth and low bioluminescence background at the lo...

The COVID-19 pandemic is exacerbating plastic pollution. A shift in waste management practices is thus urgently needed to close the plastic loop, requiring governments, researchers and industries working towards intelligent design and sustainable upcycling.

Hydrogen is a promising low carbon fuel option with geographically distributed production and consumption. Hence, its regional and global hydrogen supply chains (HSCs) are vital for the potential future energy markets. We present a holistic study of various options for transporting (not producing) hydrogen from both techno-economic and environmenta...

Biomass waste-derived porous carbons (BWDPCs) are a class of complex materials that are widely used in sustainable waste management and carbon capture. However, their diverse textural properties, the presence of various functional groups, and the varied temperatures and pressures to which they are subjected during CO 2 adsorption make it challengin...

Hydrogen production from wet organic wastes through supercritical water gasification (SCWG) promotes sustainable development. However, it is always time-consuming and expensive to achieve optimal SCWG conditions and suitable catalysts for different wastes to produce H2-rich syngas. Herein, we developed a unified machine learning (ML) framework to p...