Thermal Disproportionation for the Synthesis of Silicon Nanocrystals and Their Photoluminescent Properties

In the past decades, silicon nanocrystals have received vast attention and have been widely studied owing to not only their advantages including nontoxicity, high availability, and abundance but also their unique luminescent properties distinct from bulk silicon. Among the various synthetic methods of silicon nanocrystals, thermal disproportionation of silicon suboxides (often with H as another major composing element) bears the superiorities of unsophisticated equipment requirements, feasible processing conditions, and precise control of nanocrystals size and structure, which guarantee a bright industrial application prospect. In this paper, we summarize the recent progress of thermal disproportionation chemistry for the synthesis of silicon nanocrystals, with the focus on the effects of temperature, Si/O ratio, and the surface groups on the resulting silicon nanocrystals’ structure and their corresponding photoluminescent properties. Moreover, the paradigmatic application scenarios of the photoluminescent silicon nanocrystals synthesized via this method are showcased or envisioned.

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Recent Progress on the Synthesis of CF2H-Containing Derivatives

Synthesis ◽

10.1055/s-0039-1690027 ◽

2019 ◽

Vol 51 (24) ◽

pp. 4549-4567 ◽

Cited By ~ 12

Author(s):

Noam Levi ◽

Dafna Amir ◽

Eytan Gershonov ◽

Yossi Zafrani

Keyword(s):

Physicochemical Properties ◽

Recent Progress ◽

Cross Coupling ◽

Short Review ◽

Synthetic Methods ◽

The Past ◽

Palladium Catalyzed ◽

Group A

Recent years have witnessed a growing interest in the development of novel synthetic methods and new reagents for the synthesis of difluoromethylated compounds. Dozens of studies have been published on this topic each year over the past few years. These studies are focused on direct and indirect difluoromethylation of various organic functionalities via nucleophilic-, electrophilic-, radical-, carbene- or metal-mediated mechanisms. The present short review covers the very recent studies, published between mid-2017 and early 2019, on the synthesis of compounds containing a CF2H group. A brief summary of the physicochemical properties and medicinal applications of difluoromethylated compounds is also included.1 Introduction2 Nucleophilic Difluoromethylation2.1 Metal-Mediated Nucleophilic Difluoromethylation2.2 Non-Metal Difluoromethyl Nucleophiles3 Radical Difluoromethylation3.1 Metal-Induced Radical Difluoromethylation3.2 Non-Metal-Induced Radical Difluoromethylation3.3 Electrochemically Induced Radical Difluoromethylation4 Carbene-Based Difluoromethylation4.1 Metal-Induced Carbene Difluoromethylation4.2 Non-Metal-Induced Difluoromethyl Carbenes5 Cross-Coupling Difluoromethylation5.1 Palladium-Catalyzed Difluoromethylation5.2 Nickel-Catalyzed Difluoromethylation5.3 Copper-Mediated Difluoromethylation5.4 Iron-Catalyzed Difluoromethylation5.5 Gold-Mediated Difluoromethylation6 Electrophilic Difluoromethylation7 Other Examples7.1 A Difluoromethyl-Borane Complex7.2 A Tellurium Difluoromethyl Derivative8 Summary

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Polydopamine used as Hollow Capsule and Core–Shell Structures for Multiple Applications

NANO ◽

10.1142/s1793292015300030 ◽

2015 ◽

Vol 10 (05) ◽

pp. 1530003 ◽

Cited By ~ 18

Author(s):

Xinyu Cui ◽

Yuanyuan Yin ◽

Zuo Ma ◽

Yongkui Yin ◽

Yue Guan ◽

...

Keyword(s):

Recent Progress ◽

Colloidal Particles ◽

Shell Structures ◽

Core Shell ◽

Template Removal ◽

Precise Control ◽

The Past ◽

Structures And Properties ◽

Potential Applications ◽

Hollow Capsule

Polydopamine (PDA) capsule and core–shell structures with tailored structures and properties are of particular interests due to their multifunctions and potential applications as new colloidal structures in diverse fields. Among the available fabrication methods, PDA film onto colloidal particles followed by selective template removal has attracted extensive attention due to its advantages of precise control over the size, wall thickness and functions of the obtained capsules. The past several years has witnessed a rapid increase of research concerning the new fabrication strategies, functionalization and applications of this kind of capsules and core–shell structures, particularly in many fields such as drug delivery, catalysis, antibacterial, etc. In this review, the very recent progress of the capsule and core–shell structures based on PDA are summarized. There are basically two sections, including the fabrication process of PDA capsules, core–shell structures, and the various applications based on PDA.

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Silicon-Based Light Sources for Silicon Integrated Circuits

Advances in Optical Technologies ◽

10.1155/2008/416926 ◽

2008 ◽

Vol 2008 ◽

pp. 1-12 ◽

Cited By ~ 35

Author(s):

L. Pavesi

Keyword(s):

Integrated Circuits ◽

Silicon Nanocrystals ◽

Light Sources ◽

Bulk Silicon ◽

Light Emitters ◽

The Past ◽

Silicon Integrated Circuits ◽

Silicon Based ◽

Physical Reasons ◽

Near Future

Silicon the material per excellence for electronics is not used for sourcing light due to the lack of efficient light emitters and lasers. In this review, after having introduced the basics on lasing, I will discuss the physical reasons why silicon is not a laser material and the approaches to make it lasing. I will start with bulk silicon, then I will discuss silicon nanocrystals and Er3+ coupled silicon nanocrystals where significant advances have been done in the past and can be expected in the near future. I will conclude with an optimistic note on silicon lasing.

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Recent Progress in Machine Learning-based Prediction of Peptide Activity for Drug Discovery

Current Topics in Medicinal Chemistry ◽

10.2174/1568026619666190122151634 ◽

2019 ◽

Vol 19 (1) ◽

pp. 4-16 ◽

Cited By ~ 6

Author(s):

Qihui Wu ◽

Hanzhong Ke ◽

Dongli Li ◽

Qi Wang ◽

Jiansong Fang ◽

...

Keyword(s):

Machine Learning ◽

Drug Discovery ◽

Large Scale ◽

Recent Progress ◽

High Specificity ◽

Learning Approaches ◽

Anticancer Peptides ◽

The Past ◽

Traditional Approaches ◽

Large Scale Screening

Over the past decades, peptide as a therapeutic candidate has received increasing attention in drug discovery, especially for antimicrobial peptides (AMPs), anticancer peptides (ACPs) and antiinflammatory peptides (AIPs). It is considered that the peptides can regulate various complex diseases which are previously untouchable. In recent years, the critical problem of antimicrobial resistance drives the pharmaceutical industry to look for new therapeutic agents. Compared to organic small drugs, peptide- based therapy exhibits high specificity and minimal toxicity. Thus, peptides are widely recruited in the design and discovery of new potent drugs. Currently, large-scale screening of peptide activity with traditional approaches is costly, time-consuming and labor-intensive. Hence, in silico methods, mainly machine learning approaches, for their accuracy and effectiveness, have been introduced to predict the peptide activity. In this review, we document the recent progress in machine learning-based prediction of peptides which will be of great benefit to the discovery of potential active AMPs, ACPs and AIPs.

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Modern Synthetic Methods for the Stereoselective Construction of 1,3-Dienes

Molecules ◽

10.3390/molecules26020249 ◽

2021 ◽

Vol 26 (2) ◽

pp. 249

Author(s):

Raquel G. Soengas ◽

Humberto Rodríguez-Solla

Keyword(s):

Organic Chemistry ◽

Natural Products ◽

Functional Group ◽

Biological Activities ◽

Synthetic Methods ◽

Bond Forming ◽

Drug Candidates ◽

The Past ◽

Wide Range

The 1,3-butadiene motif is widely found in many natural products and drug candidates with relevant biological activities. Moreover, dienes are important targets for synthetic chemists, due to their ability to give access to a wide range of functional group transformations, including a broad range of C-C bond-forming processes. Therefore, the stereoselective preparation of dienes have attracted much attention over the past decades, and the search for new synthetic protocols continues unabated. The aim of this review is to give an overview of the diverse methodologies that have emerged in the last decade, with a focus on the synthetic processes that meet the requirements of efficiency and sustainability of modern organic chemistry.

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Template-Free Self-Assembly of Two-Dimensional Polymers into Nano/Microstructured Materials

Molecules ◽

10.3390/molecules26113310 ◽

2021 ◽

Vol 26 (11) ◽

pp. 3310

Author(s):

Shengda Liu ◽

Jiayun Xu ◽

Xiumei Li ◽

Tengfei Yan ◽

Shuangjiang Yu ◽

...

Keyword(s):

Self Assembly ◽

Recent Progress ◽

Two Dimensional ◽

Subsequent Removal ◽

The Past ◽

2D Polymers ◽

Template Free ◽

Self Assembled ◽

The Relationship ◽

Polymer Tubes

In the past few decades, enormous efforts have been made to synthesize covalent polymer nano/microstructured materials with specific morphologies, due to the relationship between their structures and functions. Up to now, the formation of most of these structures often requires either templates or preorganization in order to construct a specific structure before, and then the subsequent removal of previous templates to form a desired structure, on account of the lack of “self-error-correcting” properties of reversible interactions in polymers. The above processes are time-consuming and tedious. A template-free, self-assembled strategy as a “bottom-up” route to fabricate well-defined nano/microstructures remains a challenge. Herein, we introduce the recent progress in template-free, self-assembled nano/microstructures formed by covalent two-dimensional (2D) polymers, such as polymer capsules, polymer films, polymer tubes and polymer rings.

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Hyperthermia and voluntary exhaustion: integrating models and future challenges

Applied Physiology Nutrition and Metabolism ◽

10.1139/h07-043 ◽

2007 ◽

Vol 32 (4) ◽

pp. 808-817 ◽

Cited By ~ 32

Author(s):

Stephen S. Cheung

Keyword(s):

Heat Storage ◽

Brain Temperature ◽

Voluntary Exercise ◽

Future Research ◽

Laboratory Studies ◽

Exertional Heat Illness ◽

Non Invasive ◽

The Past ◽

Future Challenges ◽

Effects Of Temperature

Over the past decade, research interest has risen on the direct effects of temperature on exercise capacity and tolerance, particular in the heat. Two major paradigms have been proposed for how hyperthermia may contribute to voluntary fatigue during exercise in the heat. One suggests that voluntary exhaustion occurs upon the approach or attainment of a critical internal temperature through impairment in a variety of physiological systems. An alternate perspective proposes that thermal inputs modulate the regulation of self-paced workload to minimize heat storage. This review seeks to summarize recent research leading to the development of these two models for hyperthermia and fatigue and explore possible bridges between them. Key areas for future research and development into voluntary exhaustion in the heat include (i) the development of valid and non-invasive means to measure brain temperature, (ii) understanding variability in perception and physiological responses to heat stress across individuals, (iii) extrapolating laboratory studies to field settings, (iv) understanding the failure in behavioural and physiological thermoregulation that leads to exertional heat illness, and (v) the integration of physiological and psychological parameters limiting voluntary exercise in the heat.

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In defence of machine learning: Debunking the myths of artificial intelligence

Europe’s Journal of Psychology ◽

10.5964/ejop.v14i4.1823 ◽

2018 ◽

Vol 14 (4) ◽

pp. 734-747 ◽

Cited By ~ 6

Author(s):

Constance de Saint Laurent

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Recent Progress ◽

Life Time ◽

Clear Understanding ◽

Social Scientists ◽

Scientific Publications ◽

The Past ◽

Potential Benefits ◽

The Media

There has been much hype, over the past few years, about the recent progress of artificial intelligence (AI), especially through machine learning. If one is to believe many of the headlines that have proliferated in the media, as well as in an increasing number of scientific publications, it would seem that AI is now capable of creating and learning in ways that are starting to resemble what humans can do. And so that we should start to hope – or fear – that the creation of fully cognisant machine might be something we will witness in our life time. However, much of these beliefs are based on deep misconceptions about what AI can do, and how. In this paper, I start with a brief introduction to the principles of AI, machine learning, and neural networks, primarily intended for psychologists and social scientists, who often have much to contribute to the debates surrounding AI but lack a clear understanding of what it can currently do and how it works. I then debunk four common myths associated with AI: 1) it can create, 2) it can learn, 3) it is neutral and objective, and 4) it can solve ethically and/or culturally sensitive problems. In a third and last section, I argue that these misconceptions represent four main dangers: 1) avoiding debate, 2) naturalising our biases, 3) deresponsibilising creators and users, and 4) missing out some of the potential uses of machine learning. I finally conclude on the potential benefits of using machine learning in research, and thus on the need to defend machine learning without romanticising what it can actually do.

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