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Despite decades of research, metallic corrosion remains a long‐standing challenge in many engineering applications. Specifically, designing a material that can resist corrosion both in abiotic as well as biotic environments remains elusive. Here a lightweight sulfur–selenium (S–Se) alloy is designed with high stiffness and ductility that can serve as an excellent corrosion‐resistant coating with protection efficiency of ≈99.9% for steel in a wide range of diverse environments. S–Se coated mild steel shows a corrosion rate that is 6–7 orders of magnitude lower than bare metal in abiotic (simulated seawater and sodium sulfate solution) and biotic (sulfate‐reducing bacterial medium) environments. The coating is strongly adhesive, mechanically robust, and demonstrates excellent damage/deformation recovery properties, which provide the added advantage of significantly reducing the probability of a defect being generated and sustained in the coating, thus improving its longevity. The high corrosion resistance of the alloy is attributed in diverse environments to its semicrystalline, nonporous, antimicrobial, and viscoelastic nature with superior mechanical performance, enabling it to successfully block a variety of diffusing species.

 

Abstract The presence of in-plane chiral effects, hence spin–orbit coupling, is evident in the changes in the photocurrent produced in a TiS 3 (001) field-effect phototransistor with left versus right circularly polarized light. The direction of the photocurrent is protected by the presence of strong spin–orbit coupling and the anisotropy of the band structure as indicated in NanoARPES measurements. Dark electronic transport measurements indicate that TiS 3 is n-type and has an electron mobility in the range of 1–6 cm 2 V −1 s −1 . I – V measurements under laser illumination indicate the photocurrent exhibits a bias directionality dependence, reminiscent of bipolar spin diode behavior. Because the TiS 3 contains no heavy elements, the presence of spin–orbit coupling must be attributed to the observed loss of inversion symmetry at the TiS 3 (001) surface. 

Protective coatings based on two dimensional materials such as graphene have gained traction for diverse applications. Their impermeability, inertness, excellent bonding with metals, and amenability to functionalization renders them as promising coatings for both abiotic and microbiologically influenced corrosion (MIC). Owing to the success of graphene coatings, the whole family of 2D materials, including hexagonal boron nitride and molybdenum disulphide are being screened to obtain other promising coatings. AI-based data-driven models can accelerate virtual screening of 2D coatings with desirable physical and chemical properties. However, lack of large experimental datasets renders training of classifiers difficult and often results in over-fitting. Generate large datasets for MIC resistance of 2D coatings is both complex and laborious. Deep learning data augmentation methods can alleviate this issue by generating synthetic electrochemical data that resembles the training data classes. Here, we investigated two different deep generative models, namely variation autoencoder (VAE) and generative adversarial network (GAN) for generating synthetic data for expanding small experimental datasets. Our model experimental system included few layered graphene over copper surfaces. The synthetic data generated using GAN displayed a greater neural network system performance (83-85% accuracy) than VAE generated synthetic data (78-80% accuracy). However, VAE data performed better (90% accuracy) than GAN data (84%-85% accuracy) when using XGBoost. Finally, we show that synthetic data based on VAE and GAN models can drive machine learning models for developing MIC resistant 2D coatings. 

The growing demand of society for gas sensors for energy-efficient environmental sensing stimulates studies of new electronic materials. Here, we investigated quasi-one-dimensional titanium trisulfide (TiS3) crystals for possible applications in chemiresistors and on-chip multisensor arrays. TiS3 nanoribbons were placed as a mat over a multielectrode chip to form an array of chemiresistive gas sensors. These sensors were exposed to isopropanol as a model analyte, which was mixed with air at low concentrations of 1–100 ppm that are below the Occupational Safety and Health Administration (OSHA) permissible exposure limit. The tests were performed at room temperature (RT), as well as with heating up to 110 °C, and under an ultraviolet (UV) radiation at λ = 345 nm. We found that the RT/UV conditions result in a n-type chemiresistive response to isopropanol, which seems to be governed by its redox reactions with chemisorbed oxygen species. In contrast, the RT conditions without a UV exposure produced a p-type response that is possibly caused by the enhancement of the electron transport scattering due to the analyte adsorption. By analyzing the vector signal from the entire on-chip multisensor array, we could distinguish isopropanol from benzene, both of which produced similar responses on individual sensors. We found that the heating up to 110 °C reduces both the sensitivity and selectivity of the sensor array. 

Bacterial proteases are a promising post-translational regulation strategy in synthetic circuits because they recognize specific amino acid degradation tags (degrons) that can be fine-tuned to modulate the degradation levels of tagged proteins. For this reason, recent efforts have been made in the search for new degrons. Here we review the up-to-date applications of degradation tags for circuit engineering in bacteria. In particular, we pay special attention to the effects of degradation bottlenecks in synthetic oscillators and introduce mathematical approaches to study queueing that enable the quantitative modelling of proteolytic queues. 

Background: The unique ability of carbon to form a wide variety of allotropic modificationshas ushered in a new era in material science. Tuning the properties of these materials by functionalizationis a must-have tool for their design customized for a specific practical use. The exponentiallygrowing computational power available to researchers allows for the prediction and thoroughunderstanding of the underlying physicochemical processes responsible for the practical propertiesof pristine and modified carbons using the methods of quantum chemistry. Method: This review focuses on the computational assessment of the influence of functionalizationon the properties of carbons and enabling desired practical properties of the new materials. The firstsection of each part of this review focuses on graphene with nearly planar units built from sp2-carbons. The second section discusses patterns of sp2-carbons rolled up into curved 3D structures in avariety of ways (fullerenes). The overview of other types of carbonaceous materials, including thosewith a high abundance of sp3-carbons, including nanodiamonds, can be found in the third section ofeach manuscript’s part. Conclusion: The computational methods are especially critical for predicting electronic properties ofmaterials such as the bandgap, conductivity, optical and photoelectronic properties, solubility, adsorptivity,the potential for catalysis, sensing, imaging, and biomedical applications. We expect thatintroduction of defects to carbonaceous materials as a type of their functionalization will be a pointof growth in this area of computational research.