Seawater is one of the most abundant natural resources on our planet. Electrolysis of seawater is not only a promising approach to produce clean hydrogen energy, but also of great significance to ...seawater desalination. The implementation of seawater electrolysis requires robust and efficient electrocatalysts that can sustain seawater splitting without chloride corrosion, especially for the anode. Here we report a three-dimensional core-shell metal-nitride catalyst consisting of NiFeN nanoparticles uniformly decorated on NiMoN nanorods supported on Ni foam, which serves as an eminently active and durable oxygen evolution reaction catalyst for alkaline seawater electrolysis. Combined with an efficient hydrogen evolution reaction catalyst of NiMoN nanorods, we have achieved the industrially required current densities of 500 and 1000 mA cm
at record low voltages of 1.608 and 1.709 V, respectively, for overall alkaline seawater splitting at 60 °C. This discovery significantly advances the development of seawater electrolysis for large-scale hydrogen production.
The recent shift to wellbeing, sustainability, and resilience under Industry 5.0 has prompted formal discussions that manufacturing should be human-centric – placing the wellbeing of industry workers ...at the center of manufacturing processes, instead of system-centric – only driven by efficiency and quality improvement and cost reduction. However, there is a lack of shared understanding of the essence of human-centric manufacturing, though significant research efforts exist in enhancing the physical and cognitive wellbeing of operators. Therefore, this position paper presents our arguments on the concept, needs, reference model, enabling technologies and system frameworks of human-centric manufacturing, providing a relatable vision and research agenda for future work in human-centric manufacturing systems. We believe human-centric manufacturing should ultimately address human needs defined in an Industrial Human Needs Pyramid – from basic needs of safety and health to the highest level of esteem and self-actualization. In parallel, human-machine relationships will change following a 5C evolution map – from current Coexistence, Cooperation and Collaboration to future Compassion and Coevolution. As such, human-centric manufacturing systems need to have bi-directional empathy, proactive communication and collaborative intelligence for establishing trustworthy human-machine coevolution relationships, thereby leading to high-performance human-machine teams. It is suggested that future research focus should be on developing transparent, trustworthy and quantifiable technologies that provide a rewarding working environment driven by real-world needs.
•Defined the concept of human-centric manufacturing and proposed a 5-level Industrial Human Needs Pyramid that defines humans needs in manufacturing.•Proposeda 5C evolution journey of human-machine relationship that ultimately targets Compassion and Coevolution, beyond current Coexistence, Cooperation and Collaboration.•Discussed the enabling technologies for achieving human-centric manufacturing.•Proposed human-centric manufacturing system framework and human-centric human-robot collaboration framework.•Discussed the grand challenges for achieving human-centric manufacturing.
0D perovskite Cs4PbBr6 has attracted considerable attention recently because of its highly efficient green photoluminescence (PL) and highly debated opposing mechanisms: embedded CsPbBr3 nanocrystals ...versus intrinsic Br vacancy states. Here we provide sensitive but noninvasive methods that can not only directly correlate luminescence with the underlying structure, but also distinguish point defects from embedded nanostructures. We first use correlated Raman-PL as a passive structure–property method to identify the difference between emissive and nonemissive Cs4PbBr6 crystals and reveal the existence of CsPbBr3 nanocrystals in emissive Cs4PbBr6. We then employ a diamond anvil cell to probe the response of luminescence centers to hydrostatic pressure. The observations of fast red-shifting, diminishing, and eventual disappearance of both green emission and Raman exclude Br vacancies as possible luminescent centers. The resolution of this long-lasting controversy paves the way for further device applications of low dimensional perovskites, and our techniques are applied to other emerging materials.
Since the first report of the green emission of 2D all‐inorganic CsPb2Br5, its bandgap and photoluminescence (PL) origin have generated intense debate and remained controversial. After the discovery ...that PL centers occupy only specific morphological structures in CsPb2Br5, a two‐step highly sensitive and noninvasive optical technique is employed to resolve the controversy. Same‐spot Raman‐PL as a static property–structure probe reveals that CsPbBr3 nanocrystals are contributing to the green emission of CsPb2Br5; pressure‐dependent Raman‐PL with a diamond anvil cell as a dynamic probe further rules out point defects such as Br vacancies as an alternative mechanism. Optical absorption under hydrostatic pressure shows that the bandgap of CsPb2Br5 is 0.3–0.4 eV higher than previously reported values and remains nearly constant with pressure up to 2 GPa in good agreement with full‐fledged density functional theory (DFT) calculations. Using ion exchange of Br with Cl and I, it is further proved that CsPbBr3−x
Xx
(X = Cl or I) is responsible for the strong visible PL in CsPb2Br5−x
Xx
. This experimental approach is applicable to all PL‐active materials to distinguish intrinsic defects from extrinsic nanocrystals, and the findings pave the way for new design and development of highly efficient optoelectronic devices based on all‐inorganic lead halides.
Same‐spot Raman photoluminescence with two lasers in a diamond anvil cell under hydrostatic pressure reveals that CsPbBr3 nanocrystals, mostly located on the edges of CsPb2Br5 2D platelets, are responsible for CsPb2Br5's green emission. This sensitive noninvasive technique combining static and dynamic probes establishes a one‐to‐one property–structure relationship and distinguishes light emission from point defects versus nanoinclusions.
•Reviewed self-organizing manufacturing system from its origin, development, and characteristics, and discussed the new requirements towards mass personalization.•Introduced a new concept – ...self-organizing manufacturing network (SOMS) towards the new era of manufacturing automation.•Systematically discussed the three essential research dimensions of SOMN – (1) system modeling and control architecture, (2) peer communication, and (3) manufacturing control and their enabling technologies.•Outlined key research focuses on advancing the technical feasibility of SOMN for mass personalization and potential research challenges.
Mass personalization is becoming a reality. It requires responsive and flexible manufacturing operations for producing individualized products in dynamic batch sizes at scale in a cost-effective way. Therefore, manufacturing systems should timely respond to meet changing demands and conditions in the factory, in the supply network, and in customer needs. However, current manufacturing systems fail to adapt to dynamic production environments via changing system configurations and production plans while maintaining stable production performance. Therefore, a manufacturing system is required to be capable of self-optimizing manufacturing operations to achieve flexible, autonomous, and error-tolerant production in the mass personalization context. In this article, we systematically reviewed the literature on Self-Organizing Manufacturing Systems (SOMS) and proposed a complete concept of Self-Organizing Manufacturing Network (SOMN) as the next-generation manufacturing automation technologies for achieving mass personalization. Our review started by tracing the roots, origin, and state-of-the-art research of SOMS and concluded that the existing SOMS work could not achieve the mass personalization goal. As a focus of this review paper, we systematically discussed self-organizing manufacturing's functional requirements to achieve mass personalization and proposed Self-Organizing Manufacturing Network. The concept, functions and essential technological system components (i.e., system modeling and control architecture, peer communications, and adaptive manufacturing control) are discussed by reviewing existing work and highlighting transferrable knowledge from other disciplines. Future research challenges are also discussed.
Eu-doped In₂O₃ nanobelts (Eu-In₂O₃ NBs) and pure In₂O₃ nanobelts (In₂O₃ NBs) are synthesized by the carbon thermal reduction method. Single nanobelt sensors are fabricated via an ion beam deposition ...system with a mesh-grid mask. The gas-sensing response properties of the Eu-In₂O₃ NB device and its undoped counterpart are investigated with several kinds of gases (including H₂S, CO, NO₂, HCHO, and C₂H₅OH) at different concentrations and different temperatures. It is found that the response of the Eu-In₂O₃ NB device to 100 ppm of H₂S is the best among these gases and the sensitivity reaches 5.74, which is five times that of pure In₂O₃ NB at 260 °C. We also found that the former has an excellent sensitive response and great selectivity to H₂S compared to the latter. Besides, there is a linear relationship between the response and H₂S concentration when its concentration changes from 5 to 100 ppm and from 100 to 1000 ppm. The response/recovery time is quite short and remains stable with an increase of H₂S concentration. These results mean that the doping of Eu can improve the gas-sensing performance of In₂O₃ NB effectually.
Single crystal SnO2 nanobelts (SnO2 NBs) and La-SnO2 nanobelts (La-SnO2 NBs) were synthesized by thermal evaporation. Both a single SnO2 NB sensor and a single La-SnO2 NB sensor were developed and ...their sensing properties were investigated. It is found that the single La-SnO2 NB sensor had a high sensitivity of 8.76 to ethanediol at a concentration of 100 ppm at 230 °C, which is the highest sensitivity of a single SnO2 NB to ethanediol among three kinds of volatile organic (VOC) liquids studied, including ethanediol, ethanol, and acetone. The La-SnO2 NBs sensor also exhibits a high sensitivity, good selectivity and long-term stability with prompt response time to ethanediol. The mechanism behind the enhanced sensing performance of La-doped SnO2 nanobelts is discussed.
SnO
2
nanobelts (NBs) have unique structural and functional properties which attract great attention in gas detecting. In this work, Eu doping is adopted to improve the gas sensitivity of pure SnO
2
..., especially to enhance the response to one single gas. The Eu-doped SnO
2
NBs, pure-SnO
2
NBs, and their single NB devices are fabricated by simple techniques. The sensing properties of the two sensors have been experimentally investigated. It is found that the two sensors possess long-term stability with rapid response performance, and Eu doping improves the electronic performance and the gas-sensing response, particularly to acetone. In addition, the effects aroused by Eu have been theoretically calculated, which indicates that Eu doping enhances the sensing performance of SnO
2
. Consequently, Eu-doped SnO
2
NBs show great potential applications in the detection of acetone.
Intravenous dexmedetomidine is known to attenuate stress response in patients undergoing laparoscopic surgery. We investigated whether the addition of the highly selective alpha-2 adrenergic agonist ...dexmedetomidine into ropivacaine for ultrasound-guided transversus abdominis plane block could inhibit stress response during laparoscopic surgery, and determined the optimal dose of dexmedetomidine in it.
One hundred and twenty-five patients undergoing laparoscopic gynecological surgery were included in this prospective and randomized double-blind study. Patients received general anesthesia with or without a total of 60 ml of 0.2% ropivacaine in combination with low (0.25 μg/kg), medium (0.50 μg/kg) or high dose (1.0 μg/kg) of dexmedetomidine for the four-quadrant transversus abdominis plane block (n = 25). The primary outcomes were stress marker levels during the operation.
One hundred and twenty patients completed the study protocol. Dexmedetomidine added to ropivacaine for transversus abdominis plane block significantly reduced serum levels of cortisol, norepinephrine, epinephrine, interleukin-6, blood glucose, mean arterial pressure and heart rate in a dose-dependent manner (P < 0.05), accompanied with decreased anesthetic and opioid consumption during the operation (P < 0.05), but the high dose of dexmedetomidine induced higher incidences of bradycardia than low or medium dose of dexmedetomidine (P < 0.05).
The addition of dexmedetomidine at the dose of 0.5 μg/kg into ropivacaine for ultrasound-guided transversus abdominis plane block is the optimal dose to inhibit stress response with limited impact on blood pressure and heart rate in patients undergoing laparoscopy gynecological surgery.
This study was registered at www.chictr.org.cn on November 6th, 2016 (ChiCTR-IOR-16009753).
Mass personalization is an achievable manufacturing paradigm, which requires flexible and responsible manufacturing operations in response to dynamic batch sizes of personalized products. A ...Self-Organizing Manufacturing Network (SOMN) has been proposed to achieve mass personalization. A crucial aspect of SOMN is adaptive manufacturing control, and the Knowledge Graph, a powerful tool, has been recognized as a promising solution to enhance manufacturing intelligence. However, the current Knowledge Graph research mainly focuses on the modeling and ontology definition of the manufacturing environment, but neglects the interaction between manufacturing resources, the dynamic features of the manufacturing environment, and the application of the Knowledge Graph towards adaptive manufacturing control. Therefore, this paper proposes a Knowledge Graph-based semantic representation for adaptive manufacturing control under dynamic manufacturing environments. The proposed approach develops the Knowledge Graph based on historical and real-time scheduling data. Based on the established Knowledge Graph, Multi-Agent Reinforcement Learning has been introduced as an illustrative example of achieving adaptive scheduling control.
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