Browsing by Author "Sindhu, P."

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Charge-transfer interface of insulating metal-organic frameworks with metallic conduction
(Nature Research, 2022) Sindhu, P.; Ananthram, K.S.; Jain, A.; Tarafder, K.; Ballav, N.
Downsizing materials into hetero-structured thin film configurations is an important avenue to capture various interfacial phenomena. Metallic conduction at the interfaces of insulating transition metal oxides and organic molecules are notable examples, though, it remained elusive in the domain of coordination polymers including metal-organic frameworks (MOFs). MOFs are comprised of metal centers connected to organic linkers with an extended coordination geometry and potential void space. Poor orbitals overlap often makes these crystalline solids electrical insulators. Herein, we have fabricated hetero-structured thin film of a Mott and a band insulating MOFs via layer-by-layer method. Electrical transport measurements across the thin film evidenced an interfacial metallic conduction. The origin of such an unusual observation was understood by the first-principles density functional theory calculations; specifically, Bader charge analysis revealed significant accumulation and percolation of charge across the interface. We anticipate similar interfacial effects in other rationally designed hetero-structured thin films of MOFs. © 2022, The Author(s).
Insulator-to-metal-like transition in thin films of a biological metal-organic framework
(Nature Research, 2023) Sindhu, P.; Ananthram, K.S.; Jain, A.; Tarafder, K.; Ballav, N.
Temperature-induced insulator-to-metal transitions (IMTs) where the electrical resistivity can be altered by over tens of orders of magnitude are most often accompanied by structural phase transition in the system. Here, we demonstrate an insulator-to-metal-like transition (IMLT) at 333 K in thin films of a biological metal-organic framework (bio-MOF) which was generated upon an extended coordination of the cystine (dimer of amino acid cysteine) ligand with cupric ion (spin-1/2 system) – without appreciable change in the structure. Bio-MOFs are crystalline porous solids and a subclass of conventional MOFs where physiological functionalities of bio-molecular ligands along with the structural diversity can primarily be utilized for various biomedical applications. MOFs are usually electrical insulators (so as our expectation with bio-MOFs) and can be bestowed with reasonable electrical conductivity by the design. This discovery of electronically driven IMLT opens new opportunities for bio-MOFs, to emerge as strongly correlated reticular materials with thin film device functionalities. © 2023, The Author(s).
Modeling Uber Data for Predicting Features Responsible for Price Fluctuations
(Institute of Electrical and Electronics Engineers Inc., 2022) Sindhu, P.; Gupta, D.; Meghana, S.; Anand Kumar, M.
In the field of economics, the features and patterns of the transportation system, including classical modes of transportation such as subways and taxis, as well as innovative tools such as car pooling platforms(Uber, Lyft, etc), are key research topics. The study here demonstrates how an Uber dataset is, which comprises Uber's New York City data, works. Uber is an online service provider platform via internet or a mobile application that avails ride-hailing service. In essence, it matches passengers with drivers of vehicles to book a ride from one place to another. The service connects users with drivers who will drive them to their desired location. The dataset contains primary data about Uber pick-ups, including the date, time, longitude, and latitude coordinates. The paper attempts to examine data from different locations, weathers, hours, and dates (intraday and midweek) in New York City and apply time series data analysis, statistical regression on the dataset, and predict Uber ride prices. We arrive at conclusions by analyzing data using various graphs, calculating and estimating the influence of these elements on Uber riders' payment amounts, and emphasizing features that cause price fluctuation. Â© 2022 IEEE.
NITK_LEGAL at SemEval-2023 Task 6: A Hierarchical based system for identification of Rhetorical Roles in legal judgements
(Association for Computational Linguistics, 2023) Sindhu, P.; Gupta, D.; Meghana, S.; Anand Kumar, M.
The ability to automatically recognise the rhetorical roles of sentences in a legal case judgement is a crucial challenge to tackle since it can be useful for a number of activities that come later, such as summarising legal judgements and doing legal searches. The task is exigent since legal case documents typically lack structure, and their rhetorical roles could be subjective. This paper describes SemEval-2023 Task 6: LegalEval: Understanding Legal Texts, Sub-task A: Rhetorical Roles Prediction (RR). We propose a system to automatically generate rhetorical roles of all the sentences in a legal case document using Hierarchical Bi-LSTM CRF model and RoBERTa transformer. We also showcase different techniques used to manipulate dataset to generate a set of varying embeddings and train the Hierarchical Bi-LSTM CRF model to achieve better performance. Among all, model trained with the sent2vec embeddings concatenated with the handcrafted features perform better with the micro f1-score of 0.74 on test data. The dataset utilised in our task is available at 1 Â© 2023 Association for Computational Linguistics.