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The challenge of data-driven synthesis of advanced nanomaterials can be minimized by using machine learning algorithms to optimize synthesis parameters and expedite the innovation process. In this study, a high-throughput robotic platform was employed to synthesize over 1356 gold nanorods with varying aspect ratios via a seedless approach. The developed models guided us in synthesizing gold nanorods with customized morphology, resulting in highly repeatable morphological yield with quantifiable structure-modulating precursor adjustments. The study provides insight into the dynamic relationships between key structure-modulating precursors and the structural morphology of gold nanorods based on the expected aspect ratio. The high-throughput robotic platform-fabricated gold nanorods demonstrated precise aspect ratio control when spectrophotometrically investigated and further validated with the transmission electron microscopy characterization. These findings demonstrate the potential of high-throughput robot-assisted synthesis and machine learning in the synthesis optimization of gold nanorods and aided in the development of models that can aid such synthesis of as-desired gold nanorods.

Plateau’s law describes the configuration (joint angles ~120°) of soap bubbles, foams and cellular structures, but its applicability to solid films has not been explored. Here, the authors report the observation of a variant Plateau’s law in networks of nanobubbles made of 2D semiconductors, measuring largely varying joint angles due to the thickness-dependent effective surface tension.

Highlights The latest progress on the integration of flexible mechanical sensing platforms with machine learning (ML) is reviewed. The advantages, challenges, and future perspectives of the application of ML to intelligent flexible mechanical sensing technology are discussed. The fundamental working mechanisms and common types of flexible mechanical sensors are reviewed.

Real-time physiological information monitoring can predict and prevent disease, or improve treatment by early diagnosis. A comprehensive and continuous monitoring of human health requires highly integrated wearable and comfortable sensing devices. To address this need, we propose a low-cost electronic fabric-enabled multifunctional flexible sensing integration platform that includes a flexible pressure sensor for monitoring postural pressure, a humidity sensor for monitoring the humidity of the skin surface, and a flexible temperature sensor for visualizing the ambient temperature around the human body. Thanks to the unique rough surface texture, hierarchical structure, and robust electromechanical features of the MXene-modified nonwoven fabrics, the flexible pressure sensor can achieve a monitoring sensitivity of 1529.1 kPa ^−1 and a pressure range of 150 kPa, which meets the demand for human pressure detection. In addition, the unique porous structure of the fabric and the stacked multilayer structure of MXene enable the humidity sensor to exhibit extremely high monitoring sensitivity, even through clothing, and still be able to detect the humidity on the skin surface. Temperature sensors based on screen-printed thermochromic liquid crystals enable visual monitoring in the range of 0 °C–65 °C. Through further integration with flexible printed circuit board circuits, we demonstrate a proof-of-concept device that enables real-time monitoring of human physiological information such as physical pressure, humidity, and ambient temperature environment, suggesting that the device provides an excellent platform for the development of commercially viable wearable healthcare monitors.

Achieving carbon neutrality is an essential part of responding to climate change caused by the deforestation and over-exploitation of natural resources that have accompanied the development of human society. The carbon dioxide reduction reaction (CO2RR) is a promising strategy to capture and convert carbon dioxide (CO2) into value-added chemical products. However, the traditional trial-and-error method makes it expensive and time-consuming to understand the deeper mechanism behind the reaction, discover novel catalysts with superior performance and lower cost, and determine optimal support structures and electrolytes for the CO2RR. Emerging machine learning (ML) techniques provide an opportunity to integrate material science and artificial intelligence, which would enable chemists to extract the implicit knowledge behind data, be guided by the insights thereby gained, and be freed from performing repetitive experiments. In this perspective article, we focus on recent advancements in ML-participated CO2RR applications. After a brief introduction to ML techniques and the CO2RR, we first focus on ML-accelerated property prediction for potential CO2RR catalysts. Then we explore ML-aided prediction of catalytic activity and selectivity. This is followed by a discussion about ML-guided catalyst and electrode design. Next, the potential application of ML-assisted experimental synthesis for the CO2RR is discussed. Finally, we present specific challenges and opportunities, with the aim of better understanding research and advancements in using ML for the CO2RR.

Abstract Two‐dimensional transition metal dichalcogenides (2D TMDs) present promising applications in various fields such as electronics, optoelectronics, memory devices, batteries, superconductors, and hydrogen evolution reactions due to their regulable energy band structures and unique properties. For emerging spintronics applications, materials with excellent room‐temperature ferromagnetism are required. Although most transition metal compounds do not possess room‐temperature ferromagnetism on their own, they are widely modified by researchers using the emerging strategies to engineer or modulate their intrinsic properties. This paper reviews recent enhancement approaches to induce magnetism in 2D TMDs, mainly using doping, vacancy defects, composite of heterostructures, phase modulation, and adsorption, and also by electron irradiation induction, O plasma treatment, etc. On this basis, the produced effects of these methods for the introduction of magnetism into 2D TMDs are compressively summarized and constructively discussed. For perspective, research on magnetic doping techniques for 2D TMDs materials should be directed toward more reliable and efficient directions, such as exploring advanced design strategies to combine dilute magnetic semiconductors, antiferromagnetic semiconductors, and superconductors to develop new types of heterojunctions; and advancing experimentation strategies to fabricate the designed materials and enable their functionalities with simultaneously pursuing the upscalable growth methods for high‐quality monolayers to multilayers.

Abstract In the past decades, machine learning (ML) has impacted the field of electrocatalysis. Modern researchers have begun to take advantage of ML‐based data‐driven techniques to overcome the computational and experimental limitations to accelerate rational catalyst design. Hence, significant efforts have been made to perform ML to accelerate calculation and aid electrocatalyst design for CO2 reduction. This review discusses recent applications of ML to discover, design, and optimize novel electrocatalysts. First, insights into ML aided in accelerating calculation are presented. Then, ML aided in the rational design of the electrocatalyst is introduced, including establishing a data set/data source selection and validation of descriptor selection of ML algorithms validation and predictions of the model. Finally, the opportunities and future challenges are summarized for the future design of electrocatalyst for CO2 reduction with the assistance of ML.

As a typical spinel ferrite, NiFe2O4 is suitable for use in gas sensors. Herein, we report the fabrication of porous, tremella-like NiFe2O4 assembled using porous, ultrathin nanosheets via the coordination of Ni2+ and Fe2+ with 1,4-phenylenediboronic acid. The optical band gap of the NiFe2O4 is estimated to be about 1.7 eV. Furthermore, the NiFe2O4 sensor annealed at 400 °C exhibits a low detection limit of 50 ppb, a fast response/recovery time (11.6 s/41.9 s to 10 ppm toluene), good reproducibility, and long-term stability at 220 °C. The suitable sensing performances can be attributed to the good catalytic activity of NiFe2O4 to toluene oxidation. Moreover, the ultrathin nanosheets with porous structures provide a large number of active sites to significantly favor the diffusion and adsorption/desorption of toluene molecules. This current work provides an insight into fabricating NiFe2O4 using 1,4-phenylenediboronic acid, which is promising for ppb-level toluene detection.

Although individual γ-Fe2O3 and α-Fe2O3 have been widely fabricated for gas sensors, their mixed phase of α/γ-Fe2O3 might deliver excellent sensing properties. In this study, a facile solvothermal method was used to fabricate Fe-alkoxide. After thermal treatment, it was converted into γ-Fe2O3, α-Fe2O3 and their mixed-phase α/γ-Fe2O3 with a nanosheets-assembled flower-like structure. We studied the influence of calcination temperature on the phase and sensing properties on acetone detection. The α/γ-Fe2O3 which annealed at 400 °C included 18% α-Fe2O3 and it exhibited excellent sensing performance towards acetone compared to that of γ-Fe2O3 and α-Fe2O3. It showed a response of 353 to acetone with a concentration of 200 ppm, and a low limit of detection of 0.5 ppm at 160 °C. In addition, the change in responses with acetone concentration from 50 to 200 ppm shows a good linear relationship. Moreover, this material has good reproducibility and selectivity as well as a fast response time of 22 s and recovery time of 14 s to 200 ppm. Therefore, our mixed phase of α/γ-Fe2O3 possesses great prospects for acetone detection.

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.

Summary: Theoretical and first-principal studies involving twisted two-dimensional (2D) heterobilayer transition metal dichalcogenides (TMDs) predict interlayer exciton (ILE) emission energy amplitudes (the energy difference between the lowest and the highest ILE emission energies) in the range of 100–260 meV. This can be translated into an interfacial exciton periodic potential modulation depth of 100–260 meV. However, experimental studies on twisted TMD heterobilayers have reported only a narrow depth of ILE emission of up to ∼70 meV. Here, we report a wide degree of freedom twist-angle-driven indirect ILE emission in chemical vapor deposition (CVD)-grown MoS2/WS2 vertical heterobilayers (up to ∼10%, amplitude of 120 ± 30 meV). This is attributed to the close interlayer spacing between MoS2 and WS2 courtesy of their similar hexagonal crystal symmetry with an almost similar size, coupled with interlayer spacing tuneability at various twist angles. The wide degree of freedom of ILE emission opens exciting avenues for exploring intriguing phenomena in tuneable twistronics.

As a potential magnetic super adsorbent in wastewater treatment, Fe3O4 has been researched intensively up to date. However, its key problem of poor comprehensive magnetic properties is still challenging. In this work, an effective solution to this problem has been developed by a one-step carbothermal synthesis of Fe3O4 crystals, which are merited with pure-stoichiometry (FeO-phase free), high crystallinity, small-size (~10 nm), strong magnetism and sensitive magnetic response. The unveiled saturation magnetization of Fe3O4 nanoparticles reaches as high as 90.32 emu·g−1, and the fastest magnetic response time is as short as only 5 s. Such magnetic Fe3O4 super adsorbents exhibit outstanding performance when applied as an adsorbent for wastewater treatment. They can quickly and effectively adsorb methylene blue with an adsorption capacity of 62.5 mg·g−1, which is much higher than that of Fe3O4 adsorbents prepared by other methods reported in the literature. Importantly, this capacity is refreshable after removing the adsorbed methylene blue just by ultrasonic cleaning. With such combined outstanding magnetic properties and recyclable adsorption capacity, the problems associated with the conventional adsorbent solid–liquid separation could be resolved, thus making a forward development towards industrial wastewater treatment.

A miniature dosage meter for toxic gas is developed based on TaS2 nanosheets, which is capable of indicating the toxic dosage of trace level NO at room temperature. The TaS2 film-based chemiresistor shows an irreversible current response against the exposure of NO. The unique non-recovery characteristic makes the TaS2 film-based device an ideal indicator of total dosage of chronicle exposure.