Complex Manufacturing Process Research Articles

Improving access to gene therapy for rare diseases.

Effective gene therapy approaches have been developed for many rare diseases, including inborn errors of immunity and metabolism, haemoglobinopathies and inherited blindness. Despite successful pre-clinical and clinical results, these gene therapies are not widely available, primarily for non-medical reasons. Lack of commercial interest in therapies for ultra-rare diseases, costs of development and complex manufacturing processes required for advanced therapy medicinal products (ATMPs) are some of the main problems that are restricting access. The complexities and costs of navigating the regulatory environments in different jurisdictions for treatments that affect small numbers of patients is a problem unique to ATMPS for rare and ultra-rare diseases. In this Perspective, we outline some of the challenges and potential solutions that, we hope, will improve access to gene therapy for rare diseases.

MINIMIZING TEMPERATURE DEVIATIONS IN RUBBER MIXING PROCESS BY USING ARTIFICIAL NEURAL NETWORKS

ABSTRACT Rubber mixing is a complex manufacturing process that poses challenges for process control due to the high number of control variables, including mixing parameter settings, rheological behavior, compound viscosity, and batch-dependent material variations. Already small deviations from the control variables can influence the compound properties, leading to increased scrap rates. To address these challenges, this paper introduces an artificial intelligence–based approach to enhance process control in rubber mixing by predicting mixing temperatures from input variables. The proposed method uses feedforward neural networks (FFNs) to enable early identification of batch-specific temperature deviations, thereby enabling systematic improvements with each new application. The FFN was trained on a diverse dataset encompassing various rubber recipes and batches. Post-training, the FFN demonstrated remarkable accuracy, achieving a mean absolute percentage error of 1.00% on the training dataset and 1.44% on the validation dataset, thereby showcasing its efficacy in predicting temperature fluctuations within the mixing process. Consequently, the FFN can determine the relevant input variables necessary to achieve specific mixing temperatures, providing a foundation for an automated control system in rubber mixing process. This paper outlines the system architecture of the FFN tailored for rubber mixing and provides a comprehensive overview of the experimental results.

Bridging the gap: integrating AR and VR production line training to enhance furniture carpentry students’ understanding of production processes

ABSTRACT Large-scale furniture production involves complex manufacturing processes and methods. However, Taiwanese schools face limitations in terms of space and equipment, restricting students from engaging in furniture prototype creation or small-batch production. As a result, students often struggle to grasp practical aspects of large-scale furniture production, leading to a partial understanding of manufacturing processes and methods. To bridge this gap, this study introduces Augmented Reality (AR) technology and miniaturized furniture production lines in a school’s woodshop. AR can be used to guide students through miniaturized production stages, emphasizing semi-automated furniture production features. Additionally, Virtual Reality (VR) technology is combined to provide an immersive experience of real-world, large-scale furniture production, allowing students to understand processing scenarios and conditions in facilities of other countries. In an experiment involving 30 first-year students, an experimental group (15 participants) received mixed AR and VR instruction, while a control group (15 participants) received traditional teaching methods. Findings reveal that AR and VR integration enhanced students’ comprehension of differences between the school workship and overseas large-scale furniture production facilities. This strategy improved their furniture production knowledge, and fostered adaptability to various production conditions and methods.

Knowledge-based digital twin system: Using a knowlege-driven approach for manufacturing process modeling

The Knowledge-Based Digital Twin System is a digital twin system developed on the foundation of a knowledge graph, aimed at serving the complex manufacturing process. This system embraces a knowledge-driven modeling approach, aspiring to construct a digital twin model for the manufacturing process, thereby enabling precise description, management, prediction, and optimization of the process. The core of this system lies in the comprehensive knowledge graph that encapsulates all pertinent information about the manufacturing process, facilitating dynamic modeling and iteration through knowledge matching and inference within the knowledge, geometry, and decision model. This approach not only ensures consistency across models but also addresses the challenge of coupling multi-source heterogeneous information, creating a holistic and precise information model. As the manufacturing process deepens and knowledge accumulates, the model's understanding of the process progressively enhances, promoting self-evolution and continuous optimization. The developed knowledge-decision-geometry model acts as the ontological layer within the digital twin framework, laying a foundational conceptual framework for the digital twin of the manufacturing process. Validated on an aero-engine blade production line in a factory, the results demonstrate that the knowledge model, as the core driver, enables continuous self-updating of the geometric model for an accurate depiction of the entire manufacturing process, while the decision model provides deep insights for decision-makers based on knowledge. The system not only effectively controls, predicts, and optimizes the manufacturing process but also continually evolves as the process advances. This research offers a new perspective on the realization of the digital twin for the manufacturing process, providing solid theoretical support with a knowledge-driven approach.

CarbonKG: Industrial Carbon Emission Knowledge Graph-Based Modeling and Application for Carbon Traceability of Complex Manufacturing Process

Abstract In the context of the 2030 carbon dioxide emissions peak target, achieving carbon neutrality in manufacturing is essential. However, the complex and extensive supply chain, ranging from raw materials to the final product, presents significant challenges in assessing the carbon footprint throughout the lifecycle. This paper concentrates on the manufacturing process and introduces a modeling approach for quantifying and predicting carbon emissions, utilizing industrial intelligent technologies like process mining and knowledge graphs. First, it is crucial to establish a comprehensive carbon emission quantification model for each manufacturing stage, encompassing the energy, material, personnel, and carbon flow. Subsequently, this paper proposes an industrial carbon emission knowledge graph-based model (CarbonKG) to record and compute emissions at each production stage. Furthermore, process mining technology aids in analyzing the global distribution and movement of carbon emissions within the manufacturing process. Finally, this research presents a two-stage predictive approach for manufacturing process carbon emissions based on CarbonKG. The first stage involves initializing a local order carbon graph for prediction and developing a model to find similar cases. The second stage uses a graph-matching model to identify the Top-K similar order cases, using their carbon emission data for comprehensive prediction. Moreover, the feasibility of the proposed method was verified using actual production process data from industrial companies.

Microstructural evolution of multilayered AISI 316L-440C steel composites manufactured by laser powder bed fusion

This study presents a systematic study of multilayered steel-steel composites prepared via laser powder bed fusion (L-PBF) in an open-architecture additive manufacturing system equipped with a multiple powder delivery module. Stainless steel AISI 316L powder and high‑carbon stainless steel 440C powder were chosen as the model material system. The printed samples were characterized using optical microscopy, X-ray diffraction, electron backscattered diffraction, energy-dispersive X-ray spectroscopy, X-ray tomography, and micro-hardness to understand the complex manufacturing process and its influence on the microstructure and properties. The evolution of the multilayered structure was assessed, revealing that significantly more pronounced remelting occurs when the 440C powder is deposited on the 316L band, generating a thick 440C/316L transition region and a thin 440C band. Cracks were observed primarily at the transition regions, and were attributed to a “hot cracking” mechanism. The cracks were associated with significant chemical segregation along the grain boundaries at these regions, which exhibit intrinsically higher hot cracking susceptibility than the single-material bands. This was particularly prevalent for mixtures with <50 wt% SS440C (balance AISI 316L), as corroborated by thermodynamic calculations and observed crack locations. The processing-microstructure relationship was discussed in terms of the formation of defects at the transition regions. Challenges associated with crack removal in multi-material systems were discussed and guidelines for manufacturing multi-material, multilayered composites via L-PBF were provided.

Stochastic Economic Lot Scheduling via Self-Attention Based Deep Reinforcement Learning

The Stochastic Economic Lot Scheduling Problem (SELSP) is a difficult dynamic optimization problem with wide industrial applications. Traditional methods such as hyper-heuristics are manually designed based on substantial expert knowledge, which may limit their optimization performance. Recently, Deep Reinforcement Learning (DRL) is shown to be promising in automatically learning scheduling policies for SELSP. However, its performance is still quite far from that of hyper-heuristics, due to the lack of suitable deep models. In this paper, we propose a novel DRL method to learn dynamic scheduling policies for SELSP in an end-to-end fashion. Based on self-attention, our method can effectively extract useful features from raw state information, and is flexible in handling different numbers of products, which is not viable for previous methods. Experiments on a complex biopharmaceutical manufacturing process show that our method outperforms a recent DRL method and state-of-the-art hyper-heuristics. Moreover, the trained policy performs better in environments different from training with demand forecast errors and varying number of products, showing its strong robustness and generalization ability. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The Stochastic Economic Lot Scheduling Problem (SELSP) is an important problem for manufacturing enterprises, which is to optimally balance the production and inventory so as to minimize the total cost. However, SELSP is very challenging to solve due to the involvement of uncertain factors such as customer demands and machine failures. Traditional methods for solving SELSP, such as heuristic policies and hyper-heuristics, heavily rely on human experiences to design and hence the performance could be limited. This paper proposes a Deep Reinforcement Learning (DRL) based method to automatically learn scheduling policy for solving SELSP, which could alleviate the above limitation through a self-attention based feature extraction mechanism and reward based training. Experimental results on a realistic manufacturing process show that our method can deliver higher revenue than conventional manual policy and an existing DRL based method.

Label-free biosensor on chip (BoC) based on localized surface plasmon resonance for monitoring temperature and blood hemoglobin concentration

On-chip localized surface plasmon resonance (LSPR) biosensor on chip (BoC) is a type of label-free biosensor that utilizes the plasmonic resonances of metal nanostructures to detect changes in the refractive index of the local environment. This results in changes in the intensity and wavelength of the surface plasmon resonance, which can be used to quantify the presence and concentration of biomolecules such as proteins, nucleic acids, and cells. In this paper, we propose a novel on-chip device with a microfluidic channel that contains the biological fluid under test. We have obtained sharp resonance peaks in the wavelength range between 700–800 nm with a sensitivity of 509 nm R−1IU−1 which is good compared to other on-chip devices. The main advantage of our design is the less complex manufacturing process compared to other BoCs. Our design consists of a central cavity that is surrounded by silver and consists of rectangular pillar-shaped silver particles placed in the cavity. Two very promising applications of this device are label-free temperature sensing and blood hemoglobin (Hb) concentration sensing with a resolution of 0.222 nm/°C for temperature and 1. 34 nm/(g/dL) for Hb. However, it can be used for any kind of sensing application that involves refractive index changes as the sensing platform.

Process quality control through Bayesian optimization with adaptive local convergence

How to take as few experiments as possible to achieve the desired quality target is a challenging and essential topic of process quality control, especially for complex and expensive manufacturing processes with high-dimensional quality outputs. This paper proposes a Bayesian optimization (BO)-based strategy utilizing quality information with adaptive local convergence. First, a series of pretreatment setting rules of the Gaussian process model is proposed to reduce the uncertainty of BO. Then, the quality target is adopted into the Gaussian process model, and the acquisition function is improved. Finally, to balance global exploration and local exploitation and reduce the experimental cost, an adaptive criterion is designed to switch global BO search to local search. The operating conditions can be efficiently updated until the quality target is reached. Two applications with high-dimensional quality outputs are presented to demonstrate the effectiveness of the proposed method.

Monolithic Integration of GaN-Based Transistors and Micro-LED.

Micro-LED is considered an emerging display technology with significant potential for high resolution, brightness, and energy efficiency in display applications. However, its decreasing pixel size and complex manufacturing process create challenges for its integration with driving units. Recently, researchers have proposed various methods to achieve highly integrated micro-structures with driving unit. Researchers take advantage of the high performance of the transistors to achieve low power consumption, high current gain, and fast response frequency. This paper gives a review of recent studies on the new integration methods of micro-LEDs with different types of transistors, including the integration with BJT, HEMT, TFT, and MOSFET.

A self-supervised contrastive change point detection method for industrial time series

Manufacturing process monitoring is crucial to ensure production quality. This paper formulates the detection problem of abnormal changes in the manufacturing process as the change point detection (CPD) problem for the industrial temporal data. The premise of known data property and sufficient data annotations in existing CPD methods limits their application in the complex manufacturing process. Therefore, a self-supervised and non-parametric CPD method based on temporal trend-seasonal feature decomposition and contrastive learning (CoCPD) is proposed. CoCPD aims to solve CPD problem in an online manner. By bringing the representations of time series segments with similar properties in the feature space closer, our model can sensitively distinguish the change points that do not conform to either historical data distribution or temporal continuity. The proposed CoCPD is validated by a real-world body-in-white production case and compared with 10 state-of-the-art CPD methods. Overall, CoCPD achieves promising results by Precision 70.6%, Recall 68.8%, and the mean absolute error (MAE) 8.27. With the ability to rival the best offline baselines, CoCPD outperforms online baseline methods with improvements in Precision, Recall and MAE by 14.90%, 11.93% and 43.93%, respectively. Experiment results demonstrate that CoCPD can detect abnormal changes timely and accurately.

In-situ monitoring of charge and discharge process in supercapacitor with a micro-cavity Mach-Zehnder interferometer and a fiber Bragg grating

Supercapacitors are promising energy storage devices. However, efficient monitoring of charge and discharge process remains challenging. Current methods are limited by large size, high cost and complex manufacturing processes, restricting their practical application. To address this issue, a low-cost and easily fabricated micro-cavity Mach-Zehnder interferometer (MZI) combined with a Fiber Bragg grating (FBG) is proposed for real-time, in-situ monitoring of charge and discharge process in supercapacitor. The MZI sensor has a ultrahigh refractive index sensitivity of −21833 nm/RIU. In cyclic voltammetry(CV) test, a charge sensitivity of 11.49 nm/C is obtained. Furthermore, the temperature sensitivities of MZI and FBG are 1.64 nm/℃ and 0.011 nm/℃ respectively, measured in an electrolyte environment. This innovative approach enables precise and instantaneous measurement of critical parameters like capacitance and temperature throughout energy storage and release cycles. The proposed compact, flexible and low-cost fiber MZI sensor offers a highly promising solution for in-situ supercapacitor monitoring.

Broadband InBiSe3 alloy photoelectric detector from visible to terahertz

With the demand for communication, imaging, spectroscopy, and other applications, broadband detection has always been a particularly popular direction. However, the current photodetectors have the problems of relatively narrow response bands, a low sensitivity, a slow response speed, and complex manufacturing processes. In this article, the alloy material InBiSe3 is proposed to manufacture a wideband photodetector from visible to terahertz at room temperature. The noise equivalent power (NEP) of the detector is 1.37 × 10−10 W Hz−1/2 at 635 nm, 1.2 × 10−10 W Hz−1/2 at 808 nm, and 1.56 × 10−10 W Hz−1/2 at 980 nm. The device also exhibits a good response in the terahertz and millimeter-wave bands, with a NEP of 8.33 × 10−15 W Hz−1/2 at 0.023 THz, 7.03 × 10−14 W Hz−1/2 at 0.14 THz, 6.14 × 10−15 W Hz−1/2 at 0.171 THz, 1.91 × 10−14 W Hz−1/2 at 0.35 THz, and 4.04 × 10−14 W Hz−1/2 at 0.5 THz based on the electromagnetic induced potential wells effect. The response time is as fast as 10 µs. Our results demonstrate the promise of the InBiSe3 alloy for photoelectric applications and provide a method for the high performance of broadband photodetectors.

Development of a robotic cluster for automated and scalable cell therapy manufacturing

Background aimsThe production of commercial autologous cell therapies such as chimeric antigen receptor T cells requires complex manual manufacturing processes. Skilled labor costs and challenges in manufacturing scale-out have contributed to high prices for these products. MethodsWe present a robotic system that uses industry-standard cell therapy manufacturing equipment to automate the steps involved in cell therapy manufacturing. The robotic cluster consists of a robotic arm and customized modules, allowing the robot to manipulate a variety of standard cell therapy instruments and materials such as incubators, bioreactors, and reagent bags. This system enables existing manual manufacturing processes to be rapidly adapted to robotic manufacturing, without having to adopt a completely new technology platform. Proof-of-concept for the robotic cluster's expansion module was demonstrated by expanding human CD8+ T cells. ResultsThe robotic cultures showed comparable cell yields, viability, and identity to those manually performed. In addition, the robotic system was able to maintain culture sterility. ConclusionsSuch modular robotic solutions may support scale-up and scale-out of cell therapies that are developed using classical manual methods in academic laboratories and biotechnology companies. This approach offers a pathway for overcoming manufacturing challenges associated with manual processes, ultimately contributing to the broader accessibility and affordability for personalized immunotherapies.

3D-printing allows for fluid-controlled linear actuators with unconventional shapes

BackgroundPneumatic actuators are widely used in applications like (medical) robots, or prostheses. Pneumatic actuators require a complex manufacturing process and are produced in standardized dimensions to reduce costs. Over the last decade 3D-printing has emerged as a cost-effective and efficient production method in medical applications. 3D-printing can also function as a cost-efficient alternative production method for pneumatic actuators. ObjectiveThe goal of this research is to study the possibility of creating a pneumatic linear actuator with 3D-printing. Furthermore, the aim is to use the advantage of 3D-printing to create pneumatic actuators with non-circular cross-sections. MethodologyTo evaluate the performance of a 3D-printed pneumatic actuator, a test setup was designed and built to measure the leakage and sliding friction force. Furthermore, two pneumatic actuators with a non-conventional cross-sectional shape were designed and their performance was tested and compared with a 3D-printed cylindrical pneumatic actuator, since these tests only ran once, the results are more a guideline. During the manufacturing of the cylinders, no post-processing techniques were used. ResultsThe functioning of a 3D-printed circular pneumatic actuator was proven with low static leakage rates of 2.5%, low dynamic leakage rates of approximately 1%, and a maximum friction force of ▪. Furthermore, the results show that it is possible to print functioning pneumatic cylinders with a non-cylindrical concave cross-section. The non-conventional cylinders were tested up to ▪ with maximum dynamic leakage of ▪. ConclusionThis study demonstrates a method to create functional pneumatic linear actuators with 3D-printing. It was possible to create 3D-printed actuators with a conventional shape, e.g. circular and unconventional shapes e.g. stadium/oval shape and a kidney shape. The leak rates for conventional and unconventional shapes were in the same range. This opens up the world for more design freedom in pneumatic actuators.

Profiling of microbial populations present in ground beef and plant-based meat analogues

Plant-based meat analogues have gained significant popularity in recent years. Different from ground beef, plant-based meat analogues are derived from a broader range of raw ingredients and undergo more complex manufacturing processes. In this study, 16S rRNA and internal transcribed spacer sequencing techniques were employed to profile and compare the microbial populations present in ground beef (GB), soy-, and pea-based meat analogues (SBM and PBM), and their changes during refrigeration (4 °C) and ambient temperature storage (22 °C). The sequencing results illustrated remarkable microbial differences among GB, PBM, and SBM. Pseudomonas, Brochothrix, and Lactobacillus were the top dominate bacterial genera, Cladosporium, Candida, and Komagataella were the top dominant fungal genera present in GB, PBM, and SBM respectively. At the end of storage, dominant microorganisms present in meat samples, in general, were determined by meat types and storage temperatures, except for SBM. For example, in PBM, refrigeration favored the proliferation of Leuconostoc, while Lactococcus emerged as the primary genus at ambient temperature. For SBM, Lactobacillus and Komagataella were dominate bacterial and fungal species regardless of the storage temperatures. These findings provide crucial information for the development of preventive measures to ensure the microbial safety and quality of plant-based meat analogues.

Artificial Optoelectronic Synapses Based on Light‐Controllable Ferroelectric Semiconductor Memristor

AbstractNumerous synaptic devices have been explored for the next generation of energy‐efficient computing techniques. Among them, optoelectronic synaptic devices based on semiconductor/ferroelectric heterostructures have received a lot of attention lately due to their amazing parallelism, efficiency, and fault tolerance properties. However, polarizing the ferroelectric layer or gating the dielectric layer is necessary to achieve tunable synaptic functions, which generally causes an increase in energy consumption and complex manufacturing processes. Here, a simple and efficient method is demonstrated to develop a tunable optoelectronic synaptic device based on a single ferroelectric semiconductor, BiFeO3‐BaTiO3 (BF‐BT). Multi‐essential synaptic functions including short‐term plasticity, paired‐pulse facilitation, and long‐term plasticity are all satisfactorily replicated by the memristor device. More significantly, light‐controllable synaptic behaviors are realized by altering the ferroelectric polarization state of BF‐BT. Synaptic devices’ relaxation characteristics enable simulation of the effects of positive/negative emotions on learning and forgetting processes. This study highlights the potential of the ferroelectric semiconductor memristor in constructing the efficient optoelectronic synapses for future neuromorphic electronics with the ability to learn and sense optical information.

Автоматизированная система оперативной регистрации технических изменений предприятия радиоэлектронной промышленности

The modern production of radio-electronic equipment is a typical example of a make-to-order type of production. With custom-made products, each order is a unique product. The repeatability of orders in the total volume of orders is quite small compared to new orders, which leads to a large increase in the product range. As a rule, manufactured products are complex, high-tech products that can have a large structure, consist of a large number of components, and have a complex manufacturing process. Thus, the implementation of the full cycle of production of products and the preparation of design documentation requires the support of specialized software. Given the complexity of finished products and the large number of participants involved in production activities, there is an exponential growth in the volume of information. Without the availability of up-to-date data and methods for quick decision making, the development and production process will not be effective. The issues of automating the tracking of operational changes in design documentation at all stages of the production of radio-electronic equipment are considered. Automated systems responsible for managing the life cycle of products are called PLM – Product Lifecycle Management – this is a strategy for the production of industrial products that manages the product at all stages of the life cycle. Existing commercial solutions in this area have redundant functionality and are difficult to integrate into the existing information space of the enterprise. Aim. Study of the process of registration of technical changes in design documentation. Methods. The article presents the process of implementing technical changes in the form of a formal model and a functional model for implementing the changelog using the IDEF0 notation. The architecture of the electronic log of operational registration of changes in the form of diagrams of UML graphic notation is presented. Results. Models for the development of an automated system for managing the electronic journal of operational registration of changes are proposed. The software implementation of the system for managing the electronic journal of operational registration of changes at a research and production enterprise is considered. Conclusion. The automated system proposed in the study allows in a short time to bring to the production shops of the enterprise information about the proposed changes in the design of a prototype product, stop production, take the necessary corrective actions and finalize the product with minimization of losses for the production of a new prototype.

Current Regulatory Framework and Challenges for the Approval of Complex Generics in the US and the EU

Abstract: The pharmaceutical industry is witnessing a growing demand for complex generic products, which are generic versions of drugs that possess complex formulations, delivery systems, or active ingredients. However, the approval process for these complex generic products poses unique challenges compared to traditional generics. There is no specific regulatory procedure available for the approval of complex generics, unlike smallmolecule generics and biosimilars. This led to controversial arguments in the past about the scientific evidence needed for applications, which led to lengthy approval processes. The regulatory frameworks that are currently being used for complex generics are debatable and unclear. Complexity in the molecular structure, mechanism of action, route of delivery, and complex manufacturing process makes proving bioequivalence and pharmaceutical equivalence difficult. There is a need for harmonization of the regulatory framework by the agencies to help the generic manufacturers by providing scientific advice, defining the submission requirements for complex products, and fastening the approval process. : This review begins by discussing the regulatory landscape surrounding complex generic products in various regions, including the United States and Europe. It examines the specific guidelines and requirements set forth by regulatory authorities to ensure the safety, efficacy, and quality of these products. Additionally, the review explores the differences in terminology and definitions used to classify complex generics across different jurisdictions. Furthermore, it delves into the challenges faced by both regulatory agencies and pharmaceutical companies in evaluating and approving complex generic products. These challenges include establishing appropriate bioequivalence criteria, determining interchangeability with the reference product, addressing patent and exclusivity issues, and ensuring consistent quality throughout the product lifecycle. The impact of these challenges on market entry and competition is also discussed. The review highlights the need for harmonization and streamlining of regulations for complex generic products worldwide. It emphasizes the importance of clear and consistent guidelines to enable timely approvals, foster innovation, and facilitate patient access to affordable alternatives.

Micro-scale “top-down” construction of N/O Co-doped carbon aerogel particles for efficient hydrogen storage at ambient pressure

High specific surface area carbon aerogel is a kind of adsorption material with excellent comprehensive performance, while its large-scale application is faced with the bottleneck problems including high material cost and complex manufacturing process. Herein, the controllable stripping of the surface micro-scale macropore structure is realized by the control of the activation degree for polyacrylonitrile fiber, the activated fibers with micro-scale macropore structure is completely etched and stripped into three-dimensional frame structured carbon aerogel particles. The prepared carbon aerogel particles have ultra-high surface area (2732.18 m2·g−1) and pore volume (1.13 cm3·g−1) arising almost entirely from micropores (near 90 %), along with abundant surface heterogeneous elements composed by N (10.97 at%) and O (29.67 at%). Attributed to the synergistic effect of physisorption by micropore structure and chemisorption by O/N-containing surface groups, the carbon aerogel particles exhibit excellent hydrogen storage density of 3.17 wt% under ambient pressure. The facile construction strategy for carbon aerogel is proposed from ameliorative activation technology that has been industrialized, which avoids the time-consuming drying process and strict drying conditions in traditional preparation process.