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Quantum computing (QC) is a new approach to perform computations using the principles of quantum mechanics. The demonstration of quantum superiority is a significant landmark in the noisy intermediate scale quantum (NISQ) era. This review critically investigated the possible role of quantum computing in solving or enhancing climate change related studies especially focusing on the expected supremacy in some selected areas. The researchers ascertained a few major areas which could be enhanced through QC including new material and catalyst development and new material production process development through chemical simulation, new optimization techniques especially supporting machine learning, and new modeling approach for fluid dynamics pertinent to atmospheric and oceanic phenomena—key components in climate change research. The contributions in those fields will support better understanding of climate change issues and create enhanced or new climate change mitigation and adaptation opportunities. Quantum algorithms, notably quantum principal component analysis (qPCA), the Harrow, Hassidim, and Lloyd (HHL) algorithm, and least-squares quantum support vector machines (qSVM), offer exponential speedups over classical counterparts in processing large datasets, solving linear systems, and machine learning tasks. Quantum optimization techniques, including quantum annealing and Quantum Approximate Optimization Algorithm (QAOA), demonstrate superior performance in addressing complex optimization problems prevalent in climate change studies. Due to QC's current limitations, like challenges with scaling up, high error rates, and the need for more technological advancements, this review provides a balanced perspective on both the potential and the limitations of using QC to address climate change. In addition, integrating QC with existing computational systems poses challenges related to security vulnerabilities, the need for quantum-safe software, special issues in quantum software engineering, and limitations in variational quantum simulations. Finally, a quantum energy initiative to ensure sustainable quantum technologies with appropriate consideration for energy footprint is essential.

We have explored the relationship between physical properties and foaming behavior of 30 wt % aqueous monoethanolamine (MEA) solutions with three different surfactant-like additives. The additives included one commercial surfactant, cetyltrimethylammonium bromide (CTAB), and two CO2capture catalysts that behave as weak surfactants. The physical properties of interest were the additives’ affinity for the liquid–vapor interface and the viscosity, surface tension, and surface elasticity of the solutions. The physical properties were explored using molecular dynamics simulations as well as experiments. The additives’ affinity for the liquid–vapor interface played a key role in the foaming behavior by affecting the interfacial properties (surface tension and surface elasticity) of the aqueous MEA solutions. The solution containing CTAB showed the largest surface tension depression and the largest surface elasticity and resulted in the largest amount of foaming. The two catalyst solutions had almost similar surface tensions but differed in their elasticity. The catalyst solution with higher surface elasticity showed more foaming due to higher foam stability.

A simple approach was developed to computationally construct a polymer dataset by combining simplified molecular-input line-entry system (SMILES) strings of a targeted polymer backbone and a variety of molecular fragments. This method was used to create 14 polymer datasets by combining seven polymer backbones and molecules from two large molecular datasets (MOSES and QM9). Polymer backbones that were studied include four polydimethylsiloxane (PDMS) based backbones, poly(ethylene oxide) (PEO), poly(allyl glycidyl ether) (PAGE), and polyphosphazene (PPZ). The generated polymer datasets can be used for various cheminformatics tasks, including high-throughput screening for gas permeability and selectivity. This study utilized machine learning (ML) models to screen the polymers for CO2/CH4and CO2/N2gas separation using membranes. Several polymers of interest were identified. The results highlight that employing an ML model fitted to polymer selectivities leads to higher accuracy in predicting polymer selectivity compared to using the ratio of predicted permeabilities.

This article reports luminescence studies on wet-chemical route prepared YVO_4:Er^3+/Yb^3+ microdisc phosphor. The 980 nm laser excited upconversion (UC) emission intensity ratio of green to red bands is found too high to neglect the contribution from the red emission band, which is not observed normally in Er^3+/Yb^3+-doped materials. The red emission is also found absent in the downconversion emission under excitation at 316 nm. The variation of UC intensities with external temperature exhibits a well-fashioned pattern, which suggests that the H_11/22 and S_3/24 levels of Er^3+ ion are thermally coupled. The YVO_4:Er^3+/Yb^3+ phosphor has shown outstanding temperature-sensing behavior with maximum sensitivity of 0.0117  K^−1 at 400 K. This material is also employed to develop a latent fingerprint in green color. Furthermore, the present phosphor could be useful for solar cell concentrators, drug delivery, and disease therapy applications.