Olfaction is an evolutionary old sensory system, which provides sophisticated access to information about our surroundings. In particular, detecting the volatile organic compounds (VOCs) emitted during natural and artificial processes can be used as characteristic fingerprints and help to identify their source. Inspired by the biological example, artificial olfaction aims at achieving similar performance and thus digitizing the sense of smell. Here, we present the development of machine learning-enabled graphene-based electronic olfaction sensors and propose an approach to assess their olfactory performance toward VOCs. Eleven transient kinetic features extracted from the sensing response profile are utilized as their fingerprint information. By mimicking the Sniffin' Sticks test, electronic olfaction sensors exhibit high olfactory performance toward four VOC odors (clove, eucalyptus, lemon, and rose scent) in terms of odor detection threshold, odor discrimination, and identification performance. Upon exposure to binary odor mixtures, response features of electronic olfaction sensors behave more similarly to that of an individual odor, with a tendency that correlates with their ratio, resembling the overshadowing effect in human olfactory perception. Molecular dynamics simulations and density functional theory calculations results reveal competing adsorption mechanisms between odorant molecules and sensing materials. This may facilitate electronic olfaction sensor applications in some emerging fields, such as environmental monitoring or public security.

The escalating threat of environmental issues to both nature and humanity over the past two decades underscores the urgency of addressing environmental pollutants. Metal–organic frameworks (MOFs) have emerged as highly promising materials for tackling these challenges. Since their rise in popularity, extensive research has been conducted on MOFs, spanning from design and synthesis to a wide array of applications, such as environmental remediation, gas storage and separation, catalysis, sensors, biomedical and drug delivery systems, energy storage and conversion, and optoelectronic devices, etc. MOFs possess a multitude of advantageous properties such as large specific surface area, tunable porosity, diverse pore structures, multi-channel design, and molecular sieve capabilities, etc., making them particularly attractive for environmental applications. MOF-based composites inherit the excellent properties of MOFs and also exhibit unique physicochemical properties and structures. The tailoring of central coordinated metal ions in MOFs is critical for their adaptability in environmental applications. Although many reviews on monometallic, bimetallic, and polymetallic MOFs have been published, few reviews focusing on MOF-based composites with monometallic, bimetallic, and multi-metallic centers in the context of environmental pollutant treatment have been reported. This review addresses this gap by providing an in-depth overview of the recent progress in MOF-based composites, emphasizing their applications in hazardous gas sensing, electromagnetic wave absorption (EMWA), and pollutant degradation in both aqueous and atmospheric environments and highlighting the importance of the number and type of metal centers present. Additionally, the various categories of MOFs are summarized. MOF-based composites demonstrate significant promise in addressing environmental challenges, and this review provides a clear and valuable perspective on their potential in environmental applications.

This perspective addresses the topic of harnessing the tools of artificial intelligence (AI) for boosting innovation in functional materials design and engineering as well as discovering new materials for targeted applications in energy storage, biomedicine, composites, nanoelectronics or quantum technologies. It gives a current view of experts in the field, insisting on challenges and opportunities provided by the development of large materials databases, novel schemes for implementing AI into materials production and characterization as well as progress in the quest of simulating physical and chemical properties of realistic atomic models reaching the trillion atoms scale and with near ab initio accuracy.

Flavin mononucleotide sodium (FMNS) was recently reported as a highly efficient dispersant for the exfoliation of defect-free, few-layer, stabilized aqueous graphene dispersions. Most importantly, FMNS is innocuous and eco-friendly and can facilitate biomedical applications of graphene. Complementing those experimental studies, the influence of FMNS molecules on the aggregation behavior of graphene flakes in solution is investigated via all-atom molecular dynamics simulations. The stabilizing role of FMNS is demonstrated by the potential of mean force calculations for pairs of graphene flakes covered by FMNS molecules. These results indicate that the optimal amount ratio between FMNS molecules and carbon atoms in monolayer graphene is about 0.026 leading to a surface coverage of 0.34 FMNS molecules per nm² on the graphene flakes. Overall the simulations support the high efficiency of FMNS as a surfactant compared to other surfactants.

Two-dimensional conjugated metal-organic frameworks (2D c-MOFs) are emerging as unique electrode materials with great potential for electronic applications. However, traditional devices based on c-MOFs often utilize them directly in the powder or nanoparticle form, leading to weak adhesion to the device substrate and resulting in low stability and high noise levels in the final device. In this study, we present a novel approach utilizing thin c-MOFs synthesized via a general MOF nanosheet sacrifice approach, enhancing their aspect ratio and flexibility for high-performance electronic applications. The resultant benzene-based Cu-BHT nanosheets feature a thin thickness (around 5 nm) and a high aspect ratio (>100), affording Cu-BHT exceptional flexibility with a 10-fold decrease in Young's modulus (0.98 GPa) and hardness (0.09 GPa) compared to bulk Cu-BHT nanoparticles (10.79 and 0.75 GPa, respectively). This heightened flexibility enables the Cu-BHT nanosheets to conform to the channels of the electrodes, ensuring robust adhesion to the electrode substrate and improving device stability. As a proof-of-concept, the chemiresistive nanosensor based on Cu-BHT nanosheets demonstrates an 8.0-fold decrease in the coefficient of variation of the response intensity and a 47.1-fold increase in the signal-to-noise ratio compared to sensors based on bulk Cu-BHT nanoparticles. Combined with the machine learning algorithms, the Cu-BHT nanosensor demonstrates outstanding performance in identifying and discriminating multiple volatile organic compounds at room temperature with an average accuracy of 97.9%, surpassing the thus-far-reported chemiresistive sensors.