TiI4-doping induced bulk defects passivation in halide perovskites for high efficient photovoltaic devices

Power conversion efficiency (PCE) and stability are two important properties of perovskite solar cells (PSCs). Particularly, defects in the perovskite films could cause the generation of trap states, thereby increasing the nonradiative recombination. To address this issue, suitable dopants can be incorporated to react with non-bonded atoms or surface dangling bonds to passivate the defects. Herein, we introduced TiI₄ into CH₃NH₃PbI₃ (MAPbI₃) film and obtained a dense and uniform morphology with large crystal grains and low defect density. The champion cell based on 0.5% TiI₄-doped MAPbI₃ achieved a PCE as high as 20.55%, which is superior to those based on pristine MAPbI₃ (17.64%). Moreover, the optimal solar cell showed remarkable stability without encapsulation. It retained 88.03% of its initial PCE after 300 h of storage in ambient. This work demonstrates TiI₄ as a new and effective passivator for MAPbI₃ film.     

On the Catalytic Activity of Sn Monomers and Dimers at Graphene Edges and the Synchronized Edge Dependence of Diffusing Atoms in Sn Dimers

In this study, in situ transmission electron microscopy is performed to study the interaction between single (monomer) and paired (dimer) Sn atoms at graphene edges. The results reveal that a single Sn atom can catalyze both the growth and etching of graphene by the addition and removal of C atoms respectively. Additionally, the frequencies of the energetically favorable configurations of an Sn atom at a graphene edge, calculated using density functional theory calculations, are compared with experimental observations and are found to be in good agreement. The remarkable dynamic processes of binary atoms (dimers) are also investigated and is the first such study to the best of the knowledge. Dimer diffusion along the graphene edges depends on the graphene edge termination. Atom pairs (dimers) involving an armchair configuration tend to diffuse with a synchronized shuffling (step-wise shift) action, while dimer diffusion at zigzag edge terminations show a strong propensity to collapse the dimer with each atom diffusing in opposite directions (monomer formation). Moreover, the data reveals the role of C feedstock availability on the choice a single Sn atom makes in terms of graphene growth or etching. This study advances the understanding single atom catalytic activity at graphene edges.     

Flexible Osteogenic Glue as an All-In-One Solution to Assist Fracture Fixation and Healing

Osteogenic glue that reproduces the natural bone composition represents the final frontier of orthopedic adhesives with the potential to revolutionize surgical strategies against comminuted fractures. However, it is difficult to achieve an all-in-one formula, which could provide flexible and reliable adhesiveness while avoiding interfering with or even promoting the healing of glued fractures. Herein, an osteogenic glue characterized by inorganic-in-organic integration between amine-modified mesoporous bioactive glass nanoparticles (AMBGN) and bioadhesive gelatin-dextran network (GelDex) is introduced as an all-in-one tool to flexibly adhere and splice bone fragments and subsequently guide fracture healing during degradation. Relying on such integration, a 4-fold improvement in cohesiveness is presented, followed by a nearly 5-fold enhancement in adhesive strength in ex vivo porcine bone samples. The reversible and re-adjustable adhesiveness also enables glue to effectively splice intricate fragments from highly comminuted fractures in the rabbit radius in an in vivo environment. Moreover, well-preserved organic–inorganic integrity during degradation of the glue guides sustained interfacial osteogenesis and achieve satisfying healing outcomes in glued fractures, as observed by the 2-fold improvement in biomechanical and radiological performance compared with commercially available cyanoacrylate adhesives. The current findings propose an all-in-one solution for the fixation of bone fragments during surgery.     

State of the art advancement in rational design of g-C3N4 photocatalyst for efficient solar fuel transformation,environmental decontamination and future perspectives

Recently, the graphite based heterogeneous photocatalysts has attained tremendous research attention in various environmental applications. Among them, the graphitic carbon nitride (g-C₃N₄) is categorized as a unique solar active particle with its outstanding intrinsic properties i.e., adequate band configuration, excellent light absorptivity and thermo-physical durability, which make it highly useful and reliable for revenue transformation and ecological concerns. Considering the intrinsic potential of g-C₃N₄ in photocatalysis, so far, no report has been done in literature for its extraordinary configuration, morphological characteristics and perspective tuning for said applications. To overcome this research gap, our primary emphasis of this review regarding photocatalysis is to provide layout as well as the advancement of visible-light-fueled materials as highly stabilized and extremely effective ones for pragmatic implementation. Thus, this existing comprehensive assessment conducts a systematic survey over visible light driven non-metal novel g-C₃N₄. The major advancement of this evaluation is the fabrication of well-designed nanosized g-C₃N₄ photocatalysts with unique configurable frameworks and compositions. Furthermore, alternative techniques in order to customize the analogue band configuration and noticeable cultivation such as metal (cation), nonmetal (anion) doping, worthy metal activating, and alloy initiation with certain semiconductors are discussed in detail. In addition to this, g-C₃N₄ photocatalytic functionalities towards photocatalytic hydrogen evolution, CO₂ photoreduction, biological metal ions deterioration as well as bacterial sanitization are also presented and discussed in detail. Therefore, we believe that such a pivotal compact assessment can provide a roadmap in several perspectives on the currently underway obstacles in the innovation of effective g-C₃N₄ catalytic design processes. Moreover, this critical assessment will ultimately serve as a useful supplement in the research area of g-C₃N₄ nanosized photocatalysts and for the researchers working on its key aspects in diverse range of natural, chemistry, engineering and environmental applications.     

Liquid-phase flash sintering 8YSZ with alkali halide sintering aids

The properties of ZrO₂: 8 mol% Y₂O₃ (8YSZ) ceramics with LiF and KCl sintering aids for liquid phase formation during electric field-assisted sintering were studied. Sintering experiments were carried out at 650 °C under 200 V cm⁻¹ AC electric field by varying current density, current application time, as well as LiF and KCl contents. Pellets sintered with KCl addition had cavities, cracks and fractures. Pellets sintered with 15 wt.% LiF, on the other hand, were homogeneous after thermal removal of LiF upon Joule heating. Low electric current densities coupled with longer application times produced homogeneous specimens. Three different stages were identified during sintering experiments: (i) LiF melting with the electric field applied at furnace temperatures lower than its melting point, (ii) shrinkage due to liquid phase formation and LiF removal, (iii) final densification due to grain growth and pore elimination. The electrical behavior and an estimate of the porosity were carried out by electrochemical impedance spectroscopy measurements.     

Survey on Neuro-Fuzzy systems and their applications in technical diagnostics and measurement

Both fuzzy logic, as the basis of many inference systems, and Neural Networks, as a powerful computational model for classification and estimation, have been used in many application fields since their birth. These two techniques are somewhat supplementary to each other in a way that what one is lacking of the other can provide. This led to the creation of Neuro-Fuzzy systems which utilize fuzzy logic to construct a complex model by extending the capabilities of Artificial Neural Networks. Generally speaking all type of systems that integrate these two techniques can be called Neuro-Fuzzy systems. Key feature of these systems is that they use input–output patterns to adjust the fuzzy sets and rules inside the model. The paper reviews the principles of a Neuro-Fuzzy system and the key methods presented in this field, furthermore provides survey on their applications for technical diagnostics and measurement.     

基于注意力机制与文本信息的用户关系抽取

随着社交媒体的发展,用户之间的关系网络对于社交媒体的分析有很大的帮助。因此,该文主要研究用户好友关系检测。以往的关于用户好友关系抽取的研究主要基于社交媒体上的结构化信息,比如其他好友关系,用户的不同属性等。但是,很多时候用户本身并没有大量的好友信息存在,同时也不一定有很多确定的属性。因此,我们希望基于用户发表的文本信息来对用户关系进行预测。不同于以往的潜在好友推荐算法,该文提出了一种基于注意力机制以及长短时记忆网络(long short-term memory,LSTM)的好友关系预测模型,将好友之间的评论分开处理,通过分析用户之间的评论来判断是否具备一定的好友关系。该模型将好友双方信息拼接后的结果作为输入,并将注意力机制应用于LSTM的输出。实验表明,用户之间的评论对于好友关系预测确实有较大的实际意义,该文提出的模型较之于多个基准系统的效果,取得了明显的提升。在不加入任何其它非文本特征的情况下,实验结果的准确率达到了77%。     

基于强化学习的HP模型优化方法研究

蛋白质结构预测问题一直是生物信息学中的重要问题。基于疏水极性模型的蛋白质二维结构预测问题是一个典型的NP难问题。目前疏水极性模型优化的方法有贪心算法、粒子群算法、遗传算法、蚁群算法和蒙特卡罗模拟方法等，但这些方法成功收敛的鲁棒性不高，容易陷入局部最优。由此提出一种基于强化学习的HP模型优化方法，利用其连续马尔可夫最优决策与最大化全局累计回报的特点，在全状态空间中，构建基于能量函数的奖赏函数，引入刚性重叠检测规则，充分挖掘生物序列中的全局进化关系，从而进行有效与稳定的预测。以3条经典论文序列和5条Uniref50序列为实验对象，与贪心算法和粒子群算法分别进行了鲁棒性、收敛性与运行时间的比较。贪心算法只能在62.5%的序列上进行收敛，该文方法能在5万次训练后稳定的在所有序列上达到了收敛。与粒子群算法相比，两者都能找到最低能量结构，但该文的运行时间较粒子群算法降低了63.9%。