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Abstract Part quality manufactured by the laser powder bed fusion process is significantly affected by porosity. Existing works of process–property relationships for porosity prediction require many experiments or computationally expensive simulations without considering environmental variations. While efforts that adopt real-time monitoring sensors can only detect porosity after its occurrence rather than predicting it ahead of time. In this study, a novel porosity detection-prediction framework is proposed based on deep learning that predicts porosity in the next layer based on thermal signatures of the previous layers. The proposed framework is validated in terms of its ability to accurately predict lack of fusion porosity using computerized tomography (CT) scans, which achieves a F1-score of 0.75. The framework presented in this work can be effectively applied to quality control in additive manufacturing. As a function of the predicted porosity positions, laser process parameters in the next layer can be adjusted to avoid more part porosity in the future or the existing porosity could be filled. If the predicted part porosity is not acceptable regardless of laser parameters, the building process can be stopped to minimize the loss. 

Abstract Transition metal dichalcogenides (TMDs) are known for their layered structure and tunable functional properties. However, a unified understanding on other transition metal chalcogenides (i.e. M 2 X) is still lacking. Here, the relatively new class of copper-based chalcogenides Cu 2 X (X = Te, Se, S) is thoroughly reported. Cu 2 X are synthesized by an unusual vapor–liquid assisted growth on a Al 2 O 3 /Cu/W stack. Liquid copper plays a significant role in synthesizing these layered systems, and sapphire assists with lateral growth and exfoliation. Similar to traditional TMDs, thickness dependent phonon signatures are observed, and high-resolution atomic images reveal the single phase Cu 2 Te that prefers to grow in lattice-matched layers. Charge transport measurements indicate a metallic nature at room temperature with a transition to a semiconducting nature at low temperatures accompanied by a phase transition, in agreement with band structure calculations. These findings establish a fundamental understanding and thrust Cu 2 Te as a flexible candidate for wide applications from photovoltaics and sensors to nanoelectronics.