Heterogeneous catalysis remains at the core of various bulk chemical manufacturing and energy conversion processes, and its revolution necessitates the hunt for new materials with ideal catalytic activities and economic feasibility. Computational high-throughput screening presents a viable solution to this challenge, as machine learning (ML) has demonstrated its great potential in accelerating such processes by providing satisfactory estimations of surface reactivity with relatively low-cost information. This review focuses on recent progress in applying ML in adsorption energy prediction, which predominantly quantifies the catalytic potential of a solid catalyst. ML models that leverage inputs from different categories and exhibit various levels of complexity are classified and discussed. At the end of the review, an outlook on the current challenges and future opportunities of ML-assisted catalyst screening is supplied. We believe that this review summarizes major achievements in accelerating catalyst discovery through ML and can inspire researchers to further devise novel strategies to accelerate materials design and, ultimately, reshape the chemical industry and energy landscape.

Antibacterial resistance is a global health threat that requires further concrete action on the part of all countries. In this context, one of the biggest concerns is whether enough new antibacterial drugs are being discovered and developed. Although several high-quality reviews on clinical antibacterial drug pipelines from a global perspective were published recently, none provides comprehensive information on original antibacterial drugs at clinical stages in China. In this review, we summarize the latest progress of novel antibacterial drugs approved for marketing and under clinical evaluation in China since 2019. Information was obtained by consulting official websites, searching commercial databases, retrieving literature, asking personnel from institutions or companies, and other means, and a considerable part of the data covered here has not been included in other reviews. As of June 30, 2023, a total of 20 antibacterial projects from 17 Chinese pharmaceutical companies or developers were identified and updated. Among them, two new antibacterial drugs that belong to traditional antibiotic classes were approved by the National Medical Products Administration (NMPA) in China in 2019 and 2021, respectively, and 18 antibacterial agents are in clinical development, with one under regulatory evaluation, five in phase-3, six in phase-2, and six in phase-1. Most of the clinical candidates are new analogs or mono-components of traditional antibacterial pharmacophore types, including two dual-acting hybrid antibiotics and a recombinant antibacterial protein. Overall, despite there being 17 antibacterial clinical candidates, our analysis indicates that there are still relatively few clinically differentiated antibacterial agents in stages of clinical development in China. Hopefully, Chinese pharmaceutical companies and institutions will develop more innovative and clinically differentiated candidates with good market potential in the future research and development (R&D) of original antibacterial drugs.

New types of aerial robots (NTARs) have found extensive applications in the military, civilian contexts, scientific research, disaster management, and various other domains. Compared with traditional aerial robots, NTARs exhibit a broader range of morphological diversity, locomotion capabilities, and enhanced operational capacities. Therefore, this study defines aerial robots with the four characteristics of morphability, biomimicry, multi-modal locomotion, and manipulator attachment as NTARs. Subsequently, this paper discusses the latest research progress in the materials and manufacturing technology, actuation technology, and perception and control technology of NTARs. Thereafter, the research status of NTAR systems is summarized, focusing on the frontier development and application cases of flapping-wing micro-air vehicles, perching aerial robots, amphibious robots, and operational aerial robots. Finally, the main challenges presented by NTARs in terms of energy, materials, and perception are analyzed, and the future development trends of NTARs are summarized in terms of size and endurance, mechatronics, and complex scenarios, providing a reference direction for the follow-up exploration of NTARs.

As the scale of urban rail transit (URT) networks expands, the study of URT resilience is essential for safe and efficient operations. This paper presents a comprehensive review of URT resilience and highlights potential trends and directions for future research. First, URT resilience is defined by three primary abilities: absorption, resistance, and recovery, and four properties: robustness, vulnerability, rapidity, and redundancy. Then, the metrics and assessment approaches for URT resilience were summarized. The metrics are divided into three categories: topology-based, characteristic-based, and performance-based, and the assessment methods are divided into four categories: topological, simulation, optimization, and data-driven. Comparisons of various metrics and assessment approaches revealed that the current research trend in URT resilience is increasingly favoring the integration of traditional methods, such as conventional complex network analysis and operations optimization theory, with new techniques like big data and intelligent computing technology, to accurately assess URT resilience. Finally, five potential trends and directions for future research were identified: analyzing resilience based on multisource data, optimizing train diagram in multiple scenarios, accurate response to passenger demand through new technologies, coupling and optimizing passenger and traffic flows, and optimal line design.

The issue of opacity within data-driven artificial intelligence (AI) algorithms has become an impediment to these algorithms’ extensive utilization, especially within sensitive domains concerning health, safety, and high profitability, such as chemical engineering (CE). In order to promote reliable AI utilization in CE, this review discusses the concept of transparency within AI utilizations, which is defined based on both explainable AI (XAI) concepts and key features from within the CE field. This review also highlights the requirements of reliable AI from the aspects of causality (i.e., the correlations between the predictions and inputs of an AI), explainability (i.e., the operational rationales of the workflows), and informativeness (i.e., the mechanistic insights of the investigating systems). Related techniques are evaluated together with state-of-the-art applications to highlight the significance of establishing reliable AI applications in CE. Furthermore, a comprehensive transparency analysis case study is provided as an example to enhance understanding. Overall, this work provides a thorough discussion of this subject matter in a way that-for the first time-is particularly geared toward chemical engineers in order to raise awareness of responsible AI utilization. With this vital missing link, AI is anticipated to serve as a novel and powerful tool that can tremendously aid chemical engineers in solving bottleneck challenges in CE.

Information on the physicochemical properties of chemical species is an important prerequisite when performing tasks such as process design and product design. However, the lack of extensive data and high experimental costs hinder the development of prediction techniques for these properties. Moreover, accuracy and predictive capabilities still limit the scope and applicability of most property estimation methods. This paper proposes a new Gaussian process-based modeling framework that aims to manage a discrete and high-dimensional input space related to molecular structure representation with the group-contribution approach. A warping function is used to map discrete input into a continuous domain in order to adjust the correlation between different compounds. Prior selection techniques, including prior elicitation and prior predictive checking, are also applied during the building procedure to provide the model with more information from previous research findings. The framework is assessed using datasets of varying sizes for 20 pure component properties. For 18 out of the 20 pure component properties, the new models are found to give improved accuracy and predictive power in comparison with other published models, with and without machine learning.

The steel industry is considered an important basic sector of the national economy, and its high energy consumption and carbon emissions make it a major contributor to climate change, especially in China. The majority of crude steel in China is produced via the energy- and carbon-intensive blast furnace-basic oxygen furnace (BF-BOF) route, which greatly relies on coking coal. In recent years, China’s steel sector has made significant progress in energy conservation and emission reduction, driven by decarbonization policies and regulations. However, due to the huge output of crude steel, the steel sector still produces 15% of the total national CO₂ emissions. The direct reduced iron (DRI) plus scrap-electric arc furnace (EAF) process is currently considered a good alternative to the conventional route as a means of reducing CO₂ emissions and the steel industry’s reliance on iron ore and coking coal, since the gas-based DRI plus scrap-EAF route is expected to be more promising than the coal-based one. Unfortunately, almost no DRI is produced in China, seriously restricting the development of the EAF route. Here, we highlight the challenges and pathways of the future development of DRI, with a focus on China. In the short term, replacing natural gas with coke oven gas (COG) and byproduct gas from the integrated refining and chemical sector is a more economically feasible and cleaner way to develop a gas-based route in China. As the energy revolution proceeds, using fossil fuels in combination with carbon capture, utilization, and storage (CCUS) and hydrogen will be a good alternative due to the relatively low cost. In the long term, DRI is expected to be produced using 100% hydrogen from renewable energy. Both the development of deep processing technologies and the invention of a novel binder are required to prepare high-quality pellets for direct reduction (DR), and further research on the one-step gas-based process is necessary.

Non-ionic deep eutectic solvents (DESs) are non-ionic designer solvents with various applications in catalysis, extraction, carbon capture, and pharmaceuticals. However, discovering new DES candidates is challenging due to a lack of efficient tools that accurately predict DES formation. The search for DES relies heavily on intuition or trial-and-error processes, leading to low success rates or missed opportunities. Recognizing that hydrogen bonds (HBs) play a central role in DES formation, we aim to identify HB features that distinguish DES from non-DES systems and use them to develop machine learning (ML) models to discover new DES systems. We first analyze the HB properties of 38 known DES and 111 known non-DES systems using their molecular dynamics (MD) simulation trajectories. The analysis reveals that DES systems have two unique features compared to non-DES systems: The DESs have ① more imbalance between the numbers of the two intra-component HBs and ② more and stronger inter-component HBs. Based on these results, we develop 30 ML models using ten algorithms and three types of HB-based descriptors. The model performance is first benchmarked using the average and minimal receiver operating characteristic (ROC)-area under the curve (AUC) values. We also analyze the importance of individual features in the models, and the results are consistent with the simulation-based statistical analysis. Finally, we validate the models using the experimental data of 34 systems. The extra trees forest model outperforms the other models in the validation, with an ROC-AUC of 0.88. Our work illustrates the importance of HBs in DES formation and shows the potential of ML in discovering new DESs.

DNA guanine (G)-quadruplexes (G4s) are unique secondary structures formed by two or more stacked G-tetrads in G-rich DNA sequences. These structures have been found to play a crucial role in highly transcribed genes, especially in cancer-related oncogenes, making them attractive targets for cancer therapeutics. Significantly, targeting oncogene promoter G4 structures has emerged as a promising strategy to address the challenge of undruggable and drug-resistant proteins, such as MYC, BCL2, KRAS, and EGFR. Natural products have long been an important source of drug discovery, particularly in the fields of cancer and infectious diseases. Noteworthy progress has recently been made in the discovery of naturally occurring DNA G4-targeting drugs. Numerous DNA G4s, such as MYC-G4, BCL2-G4, KRAS-G4, PDGFR-β-G4, VEGF-G4, and telomeric-G4, have been identified as potential targets of natural products, including berberine, telomestatin, quindoline, sanguinarine, isaindigotone, and many others. Herein, we summarize and evaluate recent advancements in natural and nature-derived DNA G4 binders, focusing on understanding the structural recognition of DNA G4s by small molecules derived from nature. We also discuss the challenges and opportunities associated with developing drugs that target DNA G4s.

Infectious diseases are a global public health problem, with emerging and re-emerging infectious diseases on the rise worldwide. Therefore, their prevention and treatment are still major challenges. Bile acids are common metabolites in both hosts and microorganisms that play a significant role in controlling the metabolism of lipids, glucose, and energy. Bile acids have historically been utilized as first-line, valuable therapeutic agents for related metabolic and hepatobiliary diseases. Notably, bile acids are the major active ingredients of cow bezoar and bear bile, which are commonly used traditional Chinese medicines (TCMs) with the therapeutic effects of clearing heat, detoxification, and relieving wind and spasm. In recent years, the promising performance of bile acids against infectious diseases has attracted attention from the scientific community. This paper reviews for the first time the biological activities, possible mechanisms, production routes, and potential applications of bile acids in the treatment and prevention of infectious diseases. Compared with previous reviews, we comprehensively summarize existing studies on the use of bile acids against infectious diseases caused by pathogenic microorganisms that are leading causes of global morbidity and mortality. In addition, to ensure a stable supply of bile acids at affordable prices, it is necessary to clarify the biosynthesis of bile acids in vivo, which will assist scientists in elucidating the accumulation of bile acids and discovering how to engineer various bile acids by means of chemosynthesis, biosynthesis, and chemoenzymatic synthesis. Finally, we explore the current challenges in the field and recommend a development strategy for bile-acid-based drugs and the sustainable production of bile acids. The presented studies suggest that bile acids are potential novel therapeutic agents for managing infectious diseases and can be artificially synthesized in a sustainable way.

The anticancer potential of quassinoids has attracted a great deal of attention for decades, and scientific data revealing their possible applications in cancer management are continuously increasing in the literature. Aside from the potent cytotoxic and antitumor properties of these degraded triterpenes, several quassinoids have exhibited synergistic effects with anticancer drugs. This article provides an overview of the potential anticancer properties of quassinoids, including their cytotoxic and antitumor activities, mechanisms of action, safety evaluation, and potential benefits in combination with anticancer drugs.

Arch bridges provide significant technical and economic benefits under suitable conditions. In particular, concrete-filled steel tubular (CFST) arch bridges and steel-reinforced concrete (SRC) arch bridges are two types of arch bridges that have gained great economic competitiveness and span growth potential due to advancements in construction technology, engineering materials, and construction equipment over the past 30 years. Under the leadership of the author, two record-breaking arch bridges—that is, the Pingnan Third Bridge (a CFST arch bridge), with a span of 560 m, and the Tian’e Longtan Bridge (an SRC arch bridge), with a span of 600 m—have been built in the past five years, embodying great technological breakthroughs in the construction of these two types of arch bridges. This paper takes these two arch bridges as examples to systematically summarize the latest technological innovations and practices in the construction of CFST arch bridges and SRC arch bridges in China. The technological innovations of CFST arch bridges include cable-stayed fastening-hanging cantilevered assembly methods, new in-tube concrete materials, in-tube concrete pouring techniques, a novel thrust abutment foundation for non-rocky terrain, and measures to reduce the quantity of temporary facilities. The technological innovations of SRC arch bridges involve arch skeleton stiffness selection, the development of encasing concrete materials, encasing concrete pouring, arch rib stress mitigation, and longitudinal reinforcement optimization. To conclude, future research focuses and development directions for these two types of arch bridges are proposed.

Bear bile has been a valuable and effective medicinal material in traditional Chinese medicine (TCM) for over 13 centuries. However, the current practice of obtaining it through bear farming is under scrutiny for its adverse impact on bear welfare. Here, we present a new approach for creating artificial bear bile (ABB) as a high-quality and sustainable alternative to natural bear bile. This study addresses the scientific challenges of creating bear bile alternatives through interdisciplinary collaborations across various fields, including resources, chemistry, biology, medicine, pharmacology, and TCM. A comprehensive efficacy assessment system that bridges the gap between TCM and modern medical terminology has been established, allowing for the systematic screening of therapeutic constituents. Through the utilization of chemical synthesis and enzyme engineering technologies, our research has achieved the environmentally friendly, large-scale production of bear bile therapeutic compounds, as well as the optimization and recomposition of ABB formulations. The resulting ABB not only closely resembles natural bear bile in its composition but also offers advantages such as consistent product quality, availability of raw materials, and independence from threatened or wild resources. Comprehensive preclinical efficacy evaluations have demonstrated the equivalence of the therapeutic effects from ABB and those from commercially available drained bear bile (DBB). Furthermore, preclinical toxicological assessment and phase I clinical trials show that the safety of ABB is on par with that of the currently used DBB. This innovative strategy can serve as a new research paradigm for developing alternatives for other endangered TCMs, thereby strengthening the integrity and sustainability of TCM.

The security of the seed industry is crucial for ensuring national food security. Currently, developed countries in Europe and America, along with international seed industry giants, have entered the Breeding 4.0 era. This era integrates biotechnology, artificial intelligence (AI), and big data information technology. In contrast, China is still in a transition period between stages 2.0 and 3.0, which primarily relies on conventional selection and molecular breeding. In the context of increasingly complex international situations, accurately identifying core issues in China’s seed industry innovation and seizing the frontier of international seed technology are strategically important. These efforts are essential for ensuring food security and revitalizing the seed industry. This paper systematically analyzes the characteristics of crop breeding data from artificial selection to intelligent design breeding. It explores the applications and development trends of AI and big data in modern crop breeding from several key perspectives. These include high-throughput phenotype acquisition and analysis, multiomics big data database and management system construction, AI-based multiomics integrated analysis, and the development of intelligent breeding software tools based on biological big data and AI technology. Based on an in-depth analysis of the current status and challenges of China’s seed industry technology development, we propose strategic goals and key tasks for China’s new generation of AI and big data-driven intelligent design breeding. These suggestions aim to accelerate the development of an intelligent-driven crop breeding engineering system that features large-scale gene mining, efficient gene manipulation, engineered variety design, and systematized biobreeding. This study provides a theoretical basis and practical guidance for the development of China’s seed industry technology.

In this paper, we propose mesoscience-guided deep learning (MGDL), a deep learning modeling approach guided by mesoscience, to study complex systems. When establishing sample dataset based on the same system evolution data, different from the operation of conventional deep learning method, MGDL introduces the treatment of the dominant mechanisms of complex system and interactions between them according to the principle of compromise in competition (CIC) in mesoscience. Mesoscience constraints are then integrated into the loss function to guide the deep learning training. Two methods are proposed for the addition of mesoscience constraints. The physical interpretability of the model-training process is improved by MGDL because guidance and constraints based on physical principles are provided. MGDL was evaluated using a bubbling bed modeling case and compared with traditional techniques. With a much smaller training dataset, the results indicate that mesoscience-constraint-based model training has distinct advantages in terms of convergence stability and prediction accuracy, and it can be widely applied to various neural network configurations. The MGDL approach proposed in this paper is a novel method for utilizing the physical background information during deep learning model training. Further exploration of MGDL will be continued in the future.

The concept of precision nutrition was first proposed almost a decade ago. Current research in precision nutrition primarily focuses on comprehending individualized variations in response to dietary intake, with little attention being given to other crucial aspects of precision nutrition. Moreover, there is a dearth of comprehensive review studies that portray the landscape and framework of precision nutrition. This review commences by tracing the historical trajectory of nutritional science, with the aim of dissecting the challenges encountered in nutrition science within the new era of disease profiles. This review also deconstructs the field of precision nutrition into four key components: the proposal of the theory for individualized nutritional requirement phenotypes; the establishment of precise methods for measuring dietary intake and evaluating nutritional status; the creation of multidimensional nutritional intervention strategies that address the aspects of what, how, and when to eat; and the construction of a pathway for the translation and integration of scientific research into healthcare practices, utilizing artificial intelligence and information platforms. Incorporating these four components, this review further discusses prospective avenues that warrant exploration to achieve the objective of enhancing health through precision nutrition.

Scientific and technological revolutions and industrial transformations have accelerated the rate of innovation in environmental engineering technologies. However, few researchers have evaluated the current status and future trends of technologies. This paper summarizes the current research status in eight major subfields of environmental engineering—water treatment, air pollution control, soil/solid waste management, environmental biotechnology, environmental engineering equipment, emerging contaminants, synergistic reduction of pollution and carbon emissions, and environmental risk and intelligent management—based on bibliometric analysis and future trends in greenization, low carbonization, and intelligentization. Disruptive technologies are further identified based on discontinuous transformation, and ten such technologies are proposed, covering general and specific fields, technical links, and value sources. Additionally, the background and key innovations in disruptive technologies are elucidated in detail. This study not only provides a scientific basis for strategic decision-making, planning, and implementation in the environmental engineering field but also offers methodological guidance for the research and determination of breakthrough technologies in other areas.

Theβ-1-6 -linked poly- N -acetylglucosamine (PNAG) polymer is a conserved surface polysaccharide produced by many bacteria, fungi, and protozoan (and even filarial) parasites. This wide-ranging expression makes PNAG an attractive target for vaccine development, as it potentially encompasses a broad range of microorganisms. Significant progress has been made in discovering important properties of the biology of PNAG expression in recent years. The molecular characterization and regulation of operons for the production of PNAG biosynthetic proteins and enzymes have been studied in many bacteria. In addition, the physiological function of PNAG has been further elucidated. PNAG-based vaccines and PNAG-targeting antibodies have shown great efficacy in preclinical research. Furthermore, clinical tests for both vaccines and antibodies have been carried out in humans and economically important animals, and the results are promising. Although it is not destined to be a smooth road, we are optimistic about new vaccines and immunotherapeutics targeting PNAG becoming validated and eventually licensed for clinical use against multiple infectious agents.

Lunar habitat construction is crucial for successful lunar exploration missions. Due to the limitations of transportation conditions, extensive global research has been conducted on lunar in situ material processing techniques in recent years. The aim of this paper is to provide a comprehensive review, precise classification, and quantitative evaluation of these approaches, focusing specifically on four main approaches: reaction solidification (RS), sintering/melting (SM), bonding solidification (BS), and confinement formation (CF). Eight key indicators have been identified for the construction of low-cost and high-performance systems to assess the feasibility of these methods: in situ material ratio, curing temperature, curing time, implementation conditions, compressive strength, tensile strength, curing dimensions, and environmental adaptability. The scoring thresholds are determined by comparing the construction requirements with the actual capabilities. Among the evaluated methods, regolith bagging has emerged as a promising option due to its high in situ material ratio, low time requirement, lack of high-temperature requirements, and minimal shortcomings, with only the compressive strength falling below the neutral score. The compressive strength still maintains a value of 2 - 3 \mathrm{M} \mathrm{P} \mathrm{a}. The proposed construction scheme utilizing regolith bags offers numerous advantages, including rapid and large-scale construction, ensured tensile strength, and reduced reliance on equipment and energy. In this study, guidelines for evaluating regolith solidification techniques are provided, and directions for improvement are offered. The proposed lunar habitat design based on regolith bags is a practical reference for future research.

Reactive transport equations in porous media are critical in various scientific and engineering disciplines, but solving these equations can be computationally expensive when exploring different scenarios, such as varying porous structures and initial or boundary conditions. The deep operator network (DeepONet) has emerged as a popular deep learning framework for solving parametric partial differential equations. However, applying the DeepONet to porous media presents significant challenges due to its limited capability to extract representative features from intricate structures. To address this issue, we propose the Porous-DeepONet, a simple yet highly effective extension of the DeepONet framework that leverages convolutional neural networks (CNNs) to learn the solution operators of parametric reactive transport equations in porous media. By incorporating CNNs, we can effectively capture the intricate features of porous media, enabling accurate and efficient learning of the solution operators. We demonstrate the effectiveness of the Porous-DeepONet in accurately and rapidly learning the solution operators of parametric reactive transport equations with various boundary conditions, multiple phases, and multi-physical fields through five examples. This approach offers significant computational savings, potentially reducing the computation time by 50-1000 times compared with the finite-element method. Our work may provide a robust alternative for solving parametric reactive transport equations in porous media, paving the way for exploring complex phenomena in porous media.

Underground salt cavern CO₂ storage (SCCS) offers the dual benefits of enabling extensive CO₂ storage and facilitating the utilization of CO₂ resources while contributing the regulation of the carbon market. Its economic and operational advantages over traditional carbon capture, utilization, and storage (CCUS) projects make SCCS a more cost-effective and flexible option. Despite the widespread use of salt caverns for storing various substances, differences exist between SCCS and traditional salt cavern energy storage in terms of gas-tightness, carbon injection, brine extraction control, long-term carbon storage stability, and site selection criteria. These distinctions stem from the unique phase change characteristics of CO₂ and the application scenarios of SCCS. Therefore, targeted and forward-looking scientific research on SCCS is imperative. This paper introduces the implementation principles and application scenarios of SCCS, emphasizing its connections with carbon emissions, carbon utilization, and renewable energy peak shaving. It delves into the operational characteristics and economic advantages of SCCS compared with other CCUS methods, and addresses associated scientific challenges. In this paper, we establish a pressure equation for carbon injection and brine extraction, that considers the phase change characteristics of CO₂, and we analyze the pressure during carbon injection. By comparing the viscosities of CO₂ and other gases, SCCS’s excellent sealing performance is demonstrated. Building on this, we develop a long-term stability evaluation model and associated indices, which analyze the impact of the injection speed and minimum operating pressure on stability. Field countermeasures to ensure stability are proposed. Site selection criteria for SCCS are established, preliminary salt mine sites suitable for SCCS are identified in China, and an initial estimate of achievable carbon storage scale in China is made at over 51.8-77.7 million tons, utilizing only 20%-30% volume of abandoned salt caverns. This paper addresses key scientific and engineering challenges facing SCCS and determines crucial technical parameters, such as the operating pressure, burial depth, and storage scale, and it offers essential guidance for implementing SCCS projects in China.

With the future substantial increase in coverage and network heterogeneity, emerging networks will encounter unprecedented security threats. Covert communication is considered a potential enhanced security and privacy solution for safeguarding future wireless networks, as it can enable monitors to detect the transmitter’s transmission behavior with a low probability, thereby ensuring the secure transmission of private information. Due to its favorable security, it is foreseeable that covert communication will be widely used in various wireless communication settings such as medical, financial, and military scenarios. However, existing covert communication methods still present many challenges toward practical applications. In particular, it is difficult to guarantee the effectiveness of covert schemes based on the randomness of eavesdropping environments, and it is challenging for legitimate users to detect weak covert signals. Considering that emerging artificial-intelligence-aided transmission technologies can open up entirely new opportunities to address the above challenges, we provide a comprehensive review of recent advances and potential research directions in the field of intelligent covert communications in this work. First, the basic concepts and performance metrics of covert communications are introduced. Then, existing effective covert communication techniques in the time, frequency, spatial, power, and modulation domains are reviewed. Finally, this paper discusses potential implementations and challenges for intelligent covert communications in future networks.

To reduce CO₂ emissions from coal-fired power plants, the development of low-carbon or carbon-free fuel combustion technologies has become urgent. As a new zero-carbon fuel, ammonia (NH₃) can be used to address the storage and transportation issues of hydrogen energy. Since it is not feasible to completely replace coal with ammonia in the short term, the development of ammonia-coal co-combustion technology at the current stage is a fast and feasible approach to reduce CO₂ emissions from coal-fired power plants. This study focuses on modifying the boiler and installing two layers of eight pure-ammonia burners in a 300-MW coal-fired power plant to achieve ammonia-coal co-combustion at proportions ranging from 20% to 10% (by heat ratio) at loads of 180- to 300-MW, respectively. The results show that, during ammonia-coal co-combustion in a 300-MW coal-fired power plant, there was a more significant change in NO_x emissions at the furnace outlet compared with that under pure-coal combustion as the boiler oxygen levels varied. Moreover, ammonia burners located in the middle part of the main combustion zone exhibited a better high-temperature reduction performance than those located in the upper part of the main combustion zone. Under all ammonia co-combustion conditions, the NH₃ concentration at the furnace outlet remained below 1 parts per million (ppm). Compared with that under pure-coal conditions, the thermal efficiency of the boiler slightly decreased (by 0.12%-0.38%) under different loads when ammonia co-combustion reached 15 t·h⁻¹. Ammonia co-combustion in coal-fired power plants is a potentially feasible technology route for carbon reduction.