In this work, we demonstrate that gas adsorption is significantly higher in edge sites of vertically aligned MoS2 compared to that of the conventional basal plane exposed MoS2 films. To compare the effect of the alignment of MoS2 on the gas adsorption properties, we synthesized three distinct MoS2 films with different alignment directions ((1) horizontally aligned MoS2 (basal plane exposed), (2) mixture of horizontally aligned MoS2 and vertically aligned layers (basal and edge exposed), and (3) vertically aligned MoS2 (edge exposed)) by using rapid sulfurization method of CVD process. Vertically aligned MoS2 film shows about 5-fold enhanced sensitivity to NO2 gas molecules compared to horizontally aligned MoS2 film. Vertically aligned MoS2 has superior resistance variation compared to horizontally aligned MoS2 even with same surface area exposed to identical concentration of gas molecules. We found that electrical response to target gas molecules correlates directly with the density of the exposed edge sites of MoS2 due to high adsorption of gas molecules onto edge sites of vertically aligned MoS2. Density functional theory (DFT) calculations corroborate the experimental results as stronger NO2 binding energies are computed for multiple configurations near the edge sites of MoS2, which verifies that electrical response to target gas molecules (NO2) correlates directly with the density of the exposed edge sites of MoS2 due to high adsorption of gas molecules onto edge sites of vertically aligned MoS2. We believe that this observation extends to other 2D TMD materials as well as MoS2 and can be applied to significantly enhance the gas sensor performance in these materials.

Superior chemical sensing performance of black phosphorus (BP) is demonstrated by comparison with MoS2 and graphene. Dynamic sensing measurements of multichannel detection show that BP displays highly sensitive, selective, and fast-responsive NO2 sensing performance compared to the other representative 2D sensing materials.

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Achieving high signal-to-noise ratio in chemical and biological sensors enables accurate detection of target analytes. Unfortunately, below the limit of detection (LOD), it becomes difficult to detect the presence of small amounts of analytes and extract useful information via any of the conventional methods. In this work, we examine the possibility of extracting “hidden signals” using deep neural network to enhance gas sensing below the LOD region. As a test case system, we conduct experiments for H2 sensing in six different metallic channels (Au, Cu, Mo, Ni, Pt, Pd) and demonstrate that deep neural network can enhance the sensing capabilities for H2 concentration below the LOD. We demonstrate that this technique could be universally used for different types of sensors and target analytes. Our approach can extract new information from the hidden signals, which can be crucial for next-generation chemical sensing applications and analytical chemistry.

Medicines based on messenger RNA (mRNA) hold immense potential, as evidenced by their rapid deployment as COVID-19 vaccines. However, worldwide distribution of mRNA molecules has been limited by their thermostability, which is fundamentally limited by the intrinsic instability of RNA molecules to a chemical degradation reaction called in-line hydrolysis. Predicting the degradation of an RNA molecule is a key task in designing more stable RNA-based therapeutics. Here, we describe a crowdsourced machine learning competition (‘Stanford OpenVaccine’) on Kaggle, involving single-nucleotide resolution measurements on 6,043 diverse 102–130-nucleotide RNA constructs that were themselves solicited through crowdsourcing on the RNA design platform Eterna. The entire experiment was completed in less than 6 months, and 41% of nucleotide-level predictions from the winning model were within experimental error of the ground truth measurement. Furthermore, these models generalized to blindly predicting orthogonal degradation data on much longer mRNA molecules (504–1,588 nucleotides) with improved accuracy compared with previously published models. These results indicate that such models can represent in-line hydrolysis with excellent accuracy, supporting their use for designing stabilized messenger RNAs. The integration of two crowdsourcing platforms, one for dataset creation and another for machine learning, may be fruitful for other urgent problems that demand scientific discovery on rapid timescales.