Metal‐Organic Frameworks and their Applications in Hydrogen and Oxygen Evolution Reactions

The hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) play a vital role in many energy storage and conversion systems, including water splitting, rechargeable metal‐air batteries, and the unitized regenerative fuel cells. The noble‐metal catalysts based on Pt, Ir, and Au are the best electrocatalysts for the HER/OER, but they suffer from high price and scarcity problems. Therefore, it is urgently necessary to develop efficient, low‐cost, and environment‐friendly non‐noble metal electrocatalysts. Metal‐organic frameworks (MOFs) are crystalline materials with porous network structure. MOFs possess various compositions, large specific surface area, tunable pore structures, and they are easily functionalized. MOFs have been widely studied and applied in many fields, such as gas adsorption/separation, drug delivery, catalysis, magnetism, and optoelectronics. Recently, MOFs‐based electrocatalysts for HER/OER have been rapidly developed. These MOFs‐based catalysts exhibit excellent catalytic performance for HER/OER, demonstrating a promising application prospect in HER/OER. In this chapter, the concept, structure, category, and synthesis of MOFs will be first introduced briefly. Then, the applications of the MOFs‐based catalysts for HER/OER in recent years will be discussed in details. Specially, the synthesis, structure, and catalytic performance for HER/OER of the MOFs‐based catalysts will be emphatically discussed

A Novel Method of Fault Diagnosis for Injection Molding Systems Based on Improved VGG16 and Machine Vision

Artificial intelligence technology has enabled the manufacturing industry and actively guided its transformation and promotion for the past few decades. Injection molding technology is a crucial procedure in mechanical engineering and manufacturing due to its adaptability and dimensional stability. An essential step in the injection molding process is quality inspection and manual visual inspection is still used in conventional quality control, but this open-loop working method has issues with subjectivity and real-time monitoring capacity. This paper proposes an integrated “processing–matching–classification–diagnosis” concept based on machine vision and deep learning that allows for efficient and intelligent diagnosis of injection molding in complex scenarios. Based on eight categories of failure images of plastic components, this paper summarizes the theoretical method of processing fault categorization and identifies the various causes of defects from injection machines and molds. A template matching mechanism based on a new concept—arbitration function Jψij—provided in this paper, matches the edge features to achieve the initial classification of plastic components images. A conventional VGG16 network is innovatively upgraded in this work in order to further classify the unqualified plastic components. The classification accuracy of this improved VGG16 reaches 96.67%, which is better than the 53.33% of the traditional network. The accuracy, responsiveness, and resilience of the quality inspection are all improved in this paper. This work enhances production safety while promoting automation and intelligence of fault diagnosis in injection molding systems. Similar technical routes can be generalized to other industrial scenarios for quality inspection problems

A Novel Method of Fault Diagnosis for Injection Molding Systems Based on Improved VGG16 and Machine Vision

Artificial intelligence technology has enabled the manufacturing industry and actively guided its transformation and promotion for the past few decades. Injection molding technology is a crucial procedure in mechanical engineering and manufacturing due to its adaptability and dimensional stability. An essential step in the injection molding process is quality inspection and manual visual inspection is still used in conventional quality control, but this open-loop working method has issues with subjectivity and real-time monitoring capacity. This paper proposes an integrated “processing–matching–classification–diagnosis” concept based on machine vision and deep learning that allows for efficient and intelligent diagnosis of injection molding in complex scenarios. Based on eight categories of failure images of plastic components, this paper summarizes the theoretical method of processing fault categorization and identifies the various causes of defects from injection machines and molds. A template matching mechanism based on a new concept—arbitration function Jψij—provided in this paper, matches the edge features to achieve the initial classification of plastic components images. A conventional VGG16 network is innovatively upgraded in this work in order to further classify the unqualified plastic components. The classification accuracy of this improved VGG16 reaches 96.67%, which is better than the 53.33% of the traditional network. The accuracy, responsiveness, and resilience of the quality inspection are all improved in this paper. This work enhances production safety while promoting automation and intelligence of fault diagnosis in injection molding systems. Similar technical routes can be generalized to other industrial scenarios for quality inspection problems

The Influence of Branched Chain Length on Different Causticized Starches for the Depression of Serpentine in the Flotation of Pentlandite

Although studies on starch have developed in polymer chemistry research, their structure-activity relationship remains indistinct in the flotation depressants field. In this work, the utilization of five types of causticized starches from different botanical sources as depressants in the flotation of pentlandite/serpentine pure mineral systems was studied. The branched chain length of the starches was quantitatively analyzed using a high-performance anion-exchange chromatography system, and the average branched chain lengths of the causticized starches were obtained. The flotation results demonstrated that the depression effect of all causticized starches on serpentine had a positive correlation with the average branched chain length. Zeta potential tests, FTIR experiments, and XPS analysis confirmed that the causticized starches with a longer branched chain were absorbed more strongly on the serpentine surface. In the present study, the influence of branched chain length on the depression effect of causticized starch was investigated, which deepened our understanding of the depression mechanism of traditional macromolecule depressants and will promote the development of new macromolecule depressants

The Influence of Branched Chain Length on Different Causticized Starches for the Depression of Serpentine in the Flotation of Pentlandite

Although studies on starch have developed in polymer chemistry research, their structure-activity relationship remains indistinct in the flotation depressants field. In this work, the utilization of five types of causticized starches from different botanical sources as depressants in the flotation of pentlandite/serpentine pure mineral systems was studied. The branched chain length of the starches was quantitatively analyzed using a high-performance anion-exchange chromatography system, and the average branched chain lengths of the causticized starches were obtained. The flotation results demonstrated that the depression effect of all causticized starches on serpentine had a positive correlation with the average branched chain length. Zeta potential tests, FTIR experiments, and XPS analysis confirmed that the causticized starches with a longer branched chain were absorbed more strongly on the serpentine surface. In the present study, the influence of branched chain length on the depression effect of causticized starch was investigated, which deepened our understanding of the depression mechanism of traditional macromolecule depressants and will promote the development of new macromolecule depressants

Nitrogen, phosphorus, and potassium fertilization to achieve expected yield and improve yield components of mung bean.

Mung bean (Vigna radiata L.) is an important edible bean in the human diet worldwide. However, its growth, development, and yield may be restricted or limited by insufficient or unbalanced nitrogen (N), phosphorus (P), and potassium (K) fertilization. Despite this, there are few long-term studies of the effects of varying levels of N, P, and K combined fertilizers and the optimal fertilization for improving mung bean yield and quality. This study was conducted to optimize the fertilization strategies for high yield and to improve yield components (pods per plant, seeds per pod, and 100-seed weight) in the Bailv9 mung bean cultivar, 23 treatments were tested in 2013-2015, using a three-factor (N, P, and K fertilizers), five-level quadratic orthogonal rotation combination design. Our studies showed that, the N, P, and K fertilizers significantly influenced the pods per plant and yield, which increased and then decreased with the increasing N, P, and K fertilizers. The 100-seed weight was significantly affected by the N and P fertilization, and it was increased consistently with the increasing N fertilizer, and decreased significantly with the increasing P fertilizer. Whereas, the seeds per pod significantly decreased with the increasing N and K fertilizers, and the P fertilizer had no significant effect on it. The NP interaction had a significant effect on yield and pods per plant at high N levels, while the NK interaction had a significant but opposite effect on yield at low N levels. The optimal fertilization conditions to obtain yield >2,141.69 kg ha-1 were 34.38-42.62 kg ha-1 N, 17.55-21.70 kg ha-1 P2O5, and 53.23-67.29 kg ha-1 K2O. Moreover, the optimal N, P, and K fertilization interval to achieve pods per plant > 23.41 and the optimal N fertilization to achieve a 100-seed weight > 6.58 g intersected with the interval for yield, but the seeds per pod did not. The fertilizer ratio for the maximum yield was N:P2O5:K2O = 1:0.5:1.59. Following three years experimentation, the optimal fertilization measures were validated in 2016-2017, the results indicated that yield increased by 19.6% than that obtained using conventional fertilization. The results of this study provide a theoretical basis and technical guidance for high-yield mung bean cultivation using the optimal fertilization measures

DataSheet1_NiCo2O4 nanoparticles rich in oxygen vacancies: Salt-Assisted preparation and boosted water splitting.docx

NiCo2O4 is a promising catalyst toward water splitting to hydrogen. However, low conductivity and limited active sites on the surfaces hinder the practical applications of NiCo2O4 in water splitting. Herein, small sized NiCo2O4 nanoparticles rich in oxygen vacancies were prepared by a simple salt-assisted method. Under the assistance of KCl, the formed NiCo2O4 nanoparticles have abundant oxygen vacancies, which can increase surface active sites and improve charge transfer efficiency. In addition, KCl can effectively limit the growth of NiCo2O4, and thus reduces its size. In comparison with NiCo2O4 without the assistance of KCl, both the richer oxygen vacancies and the reduced nanoparticle sizes are favorable for the optimal NiCo2O4-2KCl to expose more active sites and increase electrochemical active surface area. As a result, it needs only the overpotentials of 129 and 304 mV to drive hydrogen and oxygen evolution at 10 mA cm−2 in 1 M KOH, respectively. When NiCo2O4-2KCl is applied in a symmetrical water splitting cell, a voltage of ∼1.66 V is only required to achieve the current density of 10 mA cm−2. This work shows that the salt-assisted method is an efficient method of developing highly active catalysts toward water splitting to hydrogen.</p