Surface chemistry of colloidal semiconductor nanocrystals (NCs) is of paramount importance because it profoundly impacts their physical and chemical properties, processing, and performance. Herein, we report the effect of the shape of ZnS NCs in terms of nanodots, nanorods, and nanoplatelets (NPL) on the surface ligand density (LD) of the commonly used oleylamine (OLA) ligand by combining three experimental quantification techniques (e.g., thermogravimetric analysis-differential scanning calorimetry, 1H nuclear magnetic resonance spectroscopy, and inductively coupled plasma-optical emission spectrometry) with the semiempirical molecular dynamics (MD) simulations. Consistent results on the surface LD derived by the aforementioned three independent techniques were obtained, presenting an ascending order of LDdots < LDrods < LDNPLs. MD simulations reveal that the highest LD for ZnS NPLs can be attributed to their extremely flat and uniform surfaces with regular distribution of surface Zn atoms for the OLA molecules to achieve parallel and tight stacking, while for ZnS nanodots and nanorods, their surfaces may have staggered arrangement and multisteps, making it unlikely for the OLA ligand to adopt the tight ligand stacking mode. The finding revealed in this work not only sheds light on the constitution of the molecule ligand shell of NCs, which is helpful for their rational morphology control, but also provides an additional and important knob for tuning their chemical functionality.

Under the framework of the United Nations Sustainable Development Goals (SDGs), mitigating global PM2.5 exposure inequality has become a worldwide task. However, the relationship between PM2.5 exposure inequality and the SDGs remains poorly understood. In this study, we evaluated the spatial distribution of global PM2.5 exposure inequality from 2000 to 2019 on a global scale and investigated the interactions between PM2.5 exposure inequality and the SDGs. The results indicate that global PM2.5 exposure inequality is increasingly worsening. Compared with high-income and low-income regions, middle- and lower-income regions have higher levels of PM2.5 exposure inequality and face greater risks of PM2.5 exposure and associated health impacts. Our analysis of the relationship between PM2.5 exposure inequality and the SDGs reveals that the positive impact of air-SDGs related to air pollution and health, such as SDG3, SDG5, SDG8, and SDG13, on global PM2.5 exposure inequality is becoming increasingly significant. In contrast, the negative impact of SDG2, SDG7, and SDG10 on global PM2.5 exposure inequality has been growing annually. These findings provide valuable insights into the complex interactions between PM2.5 exposure inequality and the SDGs and offer a reference for future efforts to build healthier and more equitable urban air environments.

A novel oxygen evolution reaction (OER) electrocatalyst was prepared by a synthesis strategy consisting of the solvothermal growth of Ni3S2 nanostructures on Ni foam, followed by hydrothermal incorporation of Fe species (Fe-Ni3S2/Ni foam). This electrocatalyst displayed a low OER overpotential of 230 mV at 100 mAcm(-2), a low Tafel slope of 43 mVdec(-1), and constant performance at an industrially relevant current density (500 mAcm(-2)) over 100 h in a 1.0 M KOH electrolyte, despite a minor loss of Fe in the process. Based on a detailed characterization by (in situ) Raman spectroscopy, (quasi-in situ) XPS, SEM, TEM, XRD, ICP-AES, EIS, and C-dl analysis, the high OER activity and stability of Fe-Ni3S2/Ni foam were attributed to the nanostructuring of the surface in the form of stable nanosheets and to the combination of Ni3S2 granting suitable electrical conductivity with newly formed NiFe-based (oxy)hydroxides at the surface of the material providing the active sites for OER.

Colorimetric determination of trace mercury ions (Hg2+) with high reproducibility and sensitivity is urgently required for water safety and environmental monitoring. Herein, an automated robotic platform is described for high-throughput and controllable synthesis of colloidal silver nanocrystals (Ag NCs) and sensitive/selective colorimetric determination of Hg2+ in aqueous solutions. The predicted models of optical simulation are con-structed as data-driven models to evaluate the quality of Ag NCs synthesized by a wet-chemical method. Based on an experimental database derived by optical characterization, the machine-learning (ML) model which covers multiple synthesis parameters is established for uniform synthesis of Ag NCs with controllable sizes matching the classical growth model. Moreover, the database of more than 1,200 valid samples established for the optical properties of Ag NCs is digitized to correlate with their average size. Optimized manual re-synthesis of high-quality Ag NCs demonstrates the practical feasibility and scalability of established models. The prepared Ag NCs can be used directly or modified with polymer ligands for quantitative detection of Hg2+ in the linear range from 0.01 to 200 mu M with a detection limit of 3 nM. The strategy provides a scientific and effective way for the high-throughput study of nanoscale optical materials in chemical engineering and environmental monitoring.

One-dimensional semiconductor-metal heterostructures manifest improved charge separation capability, tunable band gap and increased surface area, and have been regarded as promising photocatalysts for solar-to-fuel conversion through water splitting. We herein review the synthetic strategies for the rational design and preparation of one-dimensional semiconductor-metal heterostructures with precise control in terms of size, shape, composition and function. Approaches that are capable of improving the photocatalytic performance of one-dimensional semiconductor-metal heterostructures have been proposed and analyzed The present work is expected to give the rational design of one-dimensional semiconductor-metal heterostructures and provide insights into accomplishing highly efficient photocatalysis based on such materials

Code, algorithm, and data are available to generate results that are reported in the paper and central to the main claims. 1. Code and README for data mining of synthesis parameters. 2. Code for reading in situ color characterization results. 3. Code, README and figure drawing (an example) of ML prediction by SISSO.

Emerging engineering strategies of colloidal metal-semiconductor nanorod hybrid nanostructures spanning from type, size, dimension, and location of both metal nanoparticles and semiconductors, co-catalyst, band gap structure, surface ligand to hole scavenger are elaborated symmetrically to rationalize the design of this type of intriguing materials for efficient photocatalytic applications.

Due to outstanding properties, the lead perovskites exhibit great potentials in optoelectronic applications, while still facing serious issues due to its high toxicity and instability.This necessitates the development of environmental-friendly lead-free double perovskites nanocrystals with low toxicity and tunable desired properties via size-control.In this review, we present the synthesis routines of lead-free perovskite nanocrystals, followed by detailed discussion of common characterization techniques and potential applications.This work is aimed for providing more insights in the property-tuning and synthesis of high-quality nano-size lead-free perovskites.

This review summarizes the recent advances in the development of electrocatalysts to facilitate the conversion of H 2 O, N 2 and CO 2 into essential global products, providing an in-depth understanding of the design of high-performance electrocatalysts.

Digital manufacturing of materials in a rational and designable strategy and is gaining popularity on the macro-scale . However, it is challenging to control the nanocrystal morphologies on a nano-scale. Herein, we introduce Crystputer as a state-of-the-art cyber- physical system , which will enable digital manufacture of nanocrystals through convergence of cyber and physical systems. In the cyber system, an all-programmable process with rational design including codes, working files, mathematic models, and databases is designed. In the physical system, controllable synthesis is achieved by synergistic coupling of robot-assisted synthesis and nanocrystal growth , which are benefited from the identified structure-directing agents on the macro-scale as triggers of the surface energy to control the morphology on the atomic-scale. Driven by the Crystputer efficiency, over 2,300 experiments are conducted autonomously together with in situ characterization to build up the Au nanocrystals genome. The genome architecture with logic gates is further designed for the Crystputer. It is demonstrated that the Crystputer can be trained as an experienced expert for retrosynthesis and scale-up synthesis of targeted Au nanorods by taking advantage of the machine learning prediction and logic computation. The sophisticated mechanism encompassing design-control-synthesis-characterization-computing-retrosynthesis enables this Crystputer to deliver unprecedented performance and efficiency for rational design, controllable synthesis, and retrosynthesis of target Au nanocrystals. The insights and new knowledge open an opportunity to enhance the feasibility of Crystputer for intelligent digital manufacturing of customized nanocrystals in order to facilitate the paradigm shift of data-driven materials innovation from ‘code’ to ‘nanocrystals’.

As one of the most studied strategies to harvest renewable energy such as solar energy, photochemical (PC) energy conversion using catalysts have received tremendous attention. This chapter focuses on using 2D-materials free heterostructures-based photocatalysts for harvesting and the conversion of solar energy through water splitting, CO 2 reduction reaction, and N 2 reduction reaction.

Core/shell structured metal halide perovskite nanocrystals (NCs) are emerging as a type of material with remarkable optical and electronic properties. Research into this field has been developing and expanding rapidly in recent years, with significant advances in the studies of the shell growth mechanism and in understanding of properties of these materials. Significant enhancement of both the stability and the optical performance of core/shell perovskite NCs are of particular importance for their applications in optoelectronic technologies. In this review, the recent advances in core/shell structured perovskite NCs are summarized. The band structures and configurations of core/shell perovskite NCs are elaborated, the shell classification and shell engineering approaches, such as perovskites and their derivative shells, semiconductor shell, oxide shell, polymer shell, etc. are reviewed, and the shell growth mechanisms are discussed. The prospective of these NCs in lighting and displays, solar cells, photodetectors, and other devices is discussed in the light of current knowledge, remaining challenges, and future opportunities.

Technological advancements in recent decades have greatly transformed the field of material chemistry. Juxtaposing the accentuating energy demand with the pollution associated, urgent measures are required to ensure energy maximization, while reducing the extended experimental time cycle involved in energy production. In lieu of this, the prominence of catalysts in chemical reactions, particularly energy related reactions cannot be undermined, and thus it is critical to discover and design catalyst, towards the optimization of chemical processes and generation of sustainable energy. Most recently, artificial intelligence (AI) has been incorporated into several fields, particularly in advancing catalytic processes. The integration of intensive data set, machine learning models and robotics, provides a very powerful tool in modifying material synthesis and optimization by generating multifarious dataset amenable with machine learning techniques. The employment of robots automates the process of dataset and machine learning models integration in screening intermetallic surfaces of catalyst, with extreme accuracy and swiftness comparable to a number of human researchers. Although, the utilization of robots in catalyst discovery is still in its infancy, in this review we summarize current sway of artificial intelligence in catalyst discovery, briefly describe the application of databases, machine learning models and robots in this field, with emphasis on the consolidation of these monomeric units into a tripartite flow process. We point out current trends of machine learning and hybrid models of first principle calculations (DFT) for generating dataset, which is integrable into autonomous flow process of catalyst discovery. Also, we discuss catalyst discovery for renewable energy related reactions using this tripartite flow process with predetermined descriptors.

To overcome the limited potency of energy devices such as alkaline water electrolyzers, the construction of active materials with dramatically enhanced oxygen evolution reaction (OER) performance is of great importance. Herein we developed an ion diffusion-induced doping strategy that is capable of producing Ni2+/Co2+ doped two-dimensional (2D) Au-Fe7S8 nanoplatelets (NPLs) with exceptionally high OER activity outperforming the benchmark RuO2 catalyst. The co-existence of Co and Ni in Au-Fe7S8 NPLs led to the lowest OER overpotential of 243 mV at 10 mA cm(-2) and fast kinetics with a Tafel slope of 43 mV dec(-1). Density functional theory (DFT) calculations demonstrated that Ni2+/Co2+ doping improves the binding of OOH species on the {001} surfaces of Au-Fe7S8 NPLs and lowers the Gibbs free energy of the OER process, which are beneficial to outstanding OER activity of the nanoplatelets.

Photovoltaic (PV) energy conversion uses photovoltaic devices to absorb solar irradiation and convert it into electric power. This chapter discusses solar energy conversion through PV devices using 2D-materials free heterostructures. It includes perovskite heterostructures, quantum dots-sensitized nanomaterials, and dye-sensitized nanomaterials as sunlight absorbers, emphasizing the excellent optical and optoelectronic properties of perovskite heterostructures.

Polar surfaces of ionic crystals are of growing technological importance, with implications for the efficiency of photocatalysts, gas sensors, and electronic devices. The creation of ionic nanocrystals with high percentages of polar surfaces is an option for improving their efficiency in the aforementioned applications but is hard to accomplish because they are less thermodynamically stable and prone to vanish during the growth process. Herein, we develop a strategy that is capable of producing polar surface-dominated II-VI semiconductor nanocrystals, including ZnS and CdS, from copper sulfide hexagonal nanoplates through cation exchange reactions. The obtained wurtzite ZnS hexagonal nanoplates have dominant {002} polar surfaces, occupying up to 97.8% of all surfaces. Density functional theory calculations reveal the polar surfaces can be stabilized by a charge transfer of 0.25 eV/formula from the anion-terminated surface to the cation-terminated surface, which also explains the presence of polar surfaces in the initial Cu1.75S hexagonal nanoplates with cation deficiency prior to cation exchange reactions. Experimental results showed that the HER activity could be boosted by the surface polarization of polar surface-dominated ZnS hexagonal nanoplates. We anticipate this strategy is general and could be used with other systems to prepare nanocrystals with dominant polar surfaces. Furthermore, the availability of colloidal semiconductor nanocrystals with dominant polar surfaces produced through this strategy opens a new avenue for improving their efficiency in catalysis, photocatalysis, gas sensing, and other applications.

Colloidal two-dimensional (2D) semiconductor nanocrystals are of great importance due to their remarkable optical and electronic properties. Herein, shape-controllable synthesis of 2D wurtzite CdTe nanoplatelets (NPLs) by simply tailoring the reactivity of a tellurium (Te) precursor is reported. Ribbon-, shield-, and bullet-like 2D CdTe NPLs were prepared by a stepwise conversion from CdTe magic-size nanoclusters (MSNCs) by using Te32–, Te22–, and Te2– polytellurides as the tellurium precursor, respectively. This work not only develops a synthetic strategy capable of synthesising wurtzite CdTe nanoplatelets with controlled shapes by tailoring the reactivity of tellurium precursors but also gives insights into the growth mechanisms of colloidal 2D semiconductor nanocrystals.

Three-component containing hybrid heterostructures with multiple functionalities are highly desirable but difficult to synthesize. Herein, we have demonstrated the utilization of a simple seeded-growth approach for the synthesis of a hybrid heterotrimer of iron oxide–silver sulfide–zinc sulfide (Fe3O4-Ag2S-ZnS). At first, Fe3O4-Ag was synthesized by using silver nanoparticles (Ag NPs) as a seed followed by its in situ sulfurization to produce a dimer of Fe3O4-Ag2S. This dimer was successively used as a seed under controlled experimental conditions for the synthesis of Fe3O4-Ag2S-ZnS. The availability of such trimers through this approach can shed some light on the integration of entirely different functionalities in one-particle systems.

A type of heavy-metal-free metal-semiconductor Au-ZnSe hybrid nanorod is reported by Shaobin Wang, Yunguo Li, Guohua Jia, and co-workers in article number 1902231. Au particles grow nonepitaxially on the end facets of wurtzite ZnSe nanorods producing Au-tipped ZnSe hybrid nanorods, which can be used as non-toxic and eco-friendly catalysts for hydrogen production through water splitting.

Atomically thin colloidal quasi-two-dimensional (2D) semiconductor nanoplatelets (NPLs) have attracted tremendous attention due to their excellent properties and stimulating applications.