Engineering topics

Verma, Gaurav

Publications and source records attributed to Verma, Gaurav.

3 records

DOE OSTI2024

Bifunctional Metal‐Organic Nanoballs Featuring Lewis Acidic and Basic Sites as a New Platform for One‐Pot Tandem Catalysis

Abstract The design and synthesis of polyhedra using coordination‐driven self‐assembly has been an intriguing research area for synthetic chemists. Metal‐organic polyhedra are a class of intricate molecular architectures that have garnered significant attention in the literature due to their diverse structures and potential applications. Hereby, we reportCu‐MOP, a bifunctional metal‐organic cuboctahedra built using 2,6‐dimethylpyridine‐3,5‐dicarboxylic acid and copper acetate at room temperature. The presence of both Lewis basic pyridine groups and Lewis acidic copper sites imparts catalytic activity to Cu‐MOP for the tandem one‐pot deacetalization‐Knoevenagel/Henry reactions. The effect of solvent system and time duration on the yields of the reactions was studied, and the results illustrate the promising potential of these metal‐organic cuboctahedra, also known as nanoballs for applications in catalysis.

Chemistry↗

DOE OSTI2024

Tailoring the Coordination Micro‐Environment in Nanotraps for Efficient Platinum/Palladium Separation

Abstract Recovering platinum group metals from secondary resources is crucial to meet the growing demand for high‐tech applications. Various techniques are explored, and adsorption using porous materials has emerged as a promising technology due to its efficient performance and environmental beingness. However, the challenge lies in effectively recovering and separating individual platinum group metals (PGMs) given their similar chemical properties. Herein, a breakthrough approach is presented by sophisticatedly tailoring the coordination micro‐environment in a series of aminopyridine‐based porous organic polymers, which enables the creation of platinum‐specific nanotraps for efficient separation of binary PGMs (platinum/palladium). The newly synthesized POP‐o2NH 2 ‐Py demonstrates record uptakes and selectivity toward platinum over palladium, with the amino groups adjacent to the pyridine moieties being vital in improving platinum binding performance. Further breakthrough experiments underline its remarkable ability to separate platinum and palladium. Spectroscopic analysis reveals that POP‐o2NH 2 ‐Py offers a more favorable coordination fashion to platinum ions compared to palladium ions owing to the greater interaction between N and Pt 4+ and stronger intramolecular hydrogen bonding between the amino groups and four coordinating chlorines at platinum. These findings underscore the importance of fine‐tuning the coordination micro‐environment of nanotraps through subtle modifications that can greatly enhance the selectivity toward the desired metal ions.

Chemistry↗

DOE OSTI2024

Cross-Feature Transfer Learning for Efficient Tensor Program Generation

Tuning tensor program generation involves navigating a vast search space to find optimal program transformations and measurements for a program on the target hardware. The complexity of this process is further amplified by the exponential combinations of transformations, especially in heterogeneous environments. This research addresses these challenges by introducing a novel approach that learns the joint neural network and hardware features space, facilitating knowledge transfer to new, unseen target hardware. A comprehensive analysis is conducted on the existing state-of-the-art dataset, TenSet, including a thorough examination of test split strategies and the proposal of methodologies for dataset pruning. Leveraging an attention-inspired technique, we tailor the tuning of tensor programs to embed both neural network and hardware-specific features. Notably, our approach substantially reduces the dataset size by up to 53% compared to the baseline without compromising Pairwise Comparison Accuracy (PCA). Furthermore, our proposed methodology demonstrates competitive or improved mean inference times with only 25–40% of the baseline tuning time across various networks and target hardware. The attention-based tuner can effectively utilize schedules learned from previous hardware program measurements to optimize tensor program tuning on previously unseen hardware, achieving a top-5 accuracy exceeding 90%. This research introduces a significant advancement in autotuning tensor program generation, addressing the complexities associated with heterogeneous environments and showcasing promising results regarding efficiency and accuracy.

97 MATHEMATICS AND COMPUTING↗