Role of Morphology of Platinum-Based Nanoclusters in ORR/OER Activity for Nonaqueous Li–Air Battery Applications

With potential energy storage and transportation applications, nonaqueous Li–air batteries (laboratories) have received intense interest due to their high theoretical energy density. However, the sluggish kinetics of oxygen reduction/evolution reaction (ORR/OER) limits their practical applications. In this regard, developing an efficient ORR/OER electrocatalyst for LABs is highly important for the scientific community. Herein, we propose the pristine and copper core–shell Pt-based nanoclusters (NCs) as ORR/OER catalysts with different morphologies for the Li–air battery applications. The growth patterns of intermediates formed during the possible nucleation processes have been investigated in detail. Moreover, the effect of different morphologies has also been investigated by carrying out the free energy analysis for ORR/OER. Furthermore, we show that the ORR/OER activity of the catalysts can be attributed to the varied surface distribution of platinum atoms and facets on different shaped/morphology NCs. In addition to the first-ever reporting of Pt NCs as efficient electrocatalysts for Li–air batteries, this study also communicates insight into the ORR/OER activity tunability by regulating the morphology of NCs

Machine Learning Aided Interpretable Approach for Single Nucleotide-Based DNA Sequencing using a Model Nanopore

Solid-state nanopore-based electrical detection of DNA nucleotides with the quantum tunneling technique has emerged as a powerful strategy to be the next-generation sequencing technology. However, experimental complexity has been a foremost obstacle in achieving a more accurate high-throughput analysis with industrial scalability. Here, with one of the nucleotide training data sets of a model monolayer gold nanopore, we have predicted the transmission function for all other nucleotides with root-mean-square error scores as low as 0.12 using the optimized eXtreme Gradient Boosting Regression (XGBR) model. Further, the SHapley Additive exPlanations (SHAP) analysis helped in exploring the interpretability of the XGBR model prediction and revealed the complex relationship between the molecular properties of nucleotides and their transmission functions by both global and local interpretable explanations. Hence, experimental integration of our proposed machine-learning-assisted transmission function prediction method can offer a new direction for the realization of cheap, accurate, and ultrafast DNA sequencing

Hexagonal BC₃ Electrode for a High-Voltage Al-Ion Battery

Recent progresses in the field of Al-ion batteries have given directions to look for new electrode materials that can lead toward the enhancement of battery performance. Using the dispersion-corrected density functional theory calculations, we have examined the applicability of hexagonal BC₃ as a cathode material for Al-ion battery by evaluating its stability, specific capacity, and voltage profile diagram of AlCl₄-intercalated hexagonal BC₃. Our results show that AlCl₄-intercalated BC₃ compounds are stable. We have found that there is a significant charge transfer from the BC₃ system to AlCl₄ indicating toward the oxidation of BC₃ upon intercalation reaction. Several low-energy pathways are observed for the diffusion process, and it is observed that the AlCl₄ diffusion is trouble-free in the two-dimensional plane of BC₃, having a diffusion barrier as low as 0.38 eV. Moreover, we have observed that BC₃ can provide a higher average voltage 2.41 V and specific capacity of 74.37 mAh/g. These findings suggest that BC₃ could be a promising cathode material for Al-ion batteries

Graphene-like Carbon–Nitride Monolayer: A Potential Anode Material for Na- and K‑Ion Batteries

Presently, great attention is being directed toward the development of promising electrode materials for non-lithium rechargeable batteries which have the advantages of low cost, high energy storage density, and high rate capacity for substantial renewable energy applications. In this study, we have predicted that the C₃N monolayer is a potential electrode material for Na- and K-ion batteries by first-principle calculations. The diffusion barriers are calculated to be as small as 0.03 eV for Na and 0.07 eV for K, which could lead to a very fast diffusion on the C₃N monolayer surface, implying high mobility and cycle stability for batteries. The C₃N monolayer is predicted to allow a high storage capacity of 1072 mAh/g by the inclusion of multilayer adsorption with an average voltage of 0.13 V for Na₂C₃N and 0.26 V for K₂C₃N systems, which is more promising than previously studied anode materials. All of these results ensure that the C₃N monolayer could serve as an excellent anode material for Na- and K-ion batteries

Multilayered Platinum Nanotube for Oxygen Reduction in a Fuel Cell Cathode: Origin of Activity and Product Selectivity

The practical usages of proton exchange membrane fuel cells from the economical perspective is closely related to the development of catalysts with reduced platinum loading for improved oxygen reduction reaction (ORR) activity. For this, a multilayered platinum nanotube enclosed by (111) and (100) facets has been modeled and studied for ORR activity using the density functional theory calculations. The stability of the nanotube is verified through energetic, thermal, and dynamic stability calculations. Activation barrier analysis shows that the rate-determining steps (O₂ dissociation and OH formation) are highly improved over the nanotube surface. We find that four-electron reduction pathway (for H₂O formation) is favored over two-electron reduction (for H₂O₂ formation) for the nanotube catalyst, which ensures excellent product selectivity (H₂O vs H₂O₂). The excellent catalytic activity and product selectivity of the nanotube can be attributed toward the favorable adsorption energies of ORR intermediates, as the adsorption energies of key ORR intermediates are reported to be excellent descriptors for ORR activity. Therefore, the platinum nanotube can be a potential electrode material for fuel cell and other related applications

Machine Learning Aided Interpretable Approach for Single Nucleotide-Based DNA Sequencing using a Model Nanopore

Solid-state nanopore-based electrical detection of DNA nucleotides with the quantum tunneling technique has emerged as a powerful strategy to be the next-generation sequencing technology. However, experimental complexity has been a foremost obstacle in achieving a more accurate high-throughput analysis with industrial scalability. Here, with one of the nucleotide training data sets of a model monolayer gold nanopore, we have predicted the transmission function for all other nucleotides with root-mean-square error scores as low as 0.12 using the optimized eXtreme Gradient Boosting Regression (XGBR) model. Further, the SHapley Additive exPlanations (SHAP) analysis helped in exploring the interpretability of the XGBR model prediction and revealed the complex relationship between the molecular properties of nucleotides and their transmission functions by both global and local interpretable explanations. Hence, experimental integration of our proposed machine-learning-assisted transmission function prediction method can offer a new direction for the realization of cheap, accurate, and ultrafast DNA sequencing

Machine Learning Assisted Screening of MXene with Superior Anchoring Effect in Al–S Batteries

Dissolution of polysulfide intermediates into electrolytes has been a major bottleneck in the development of the Al–S battery. MXenes can be promising anchoring materials, even though finding the most suitable candidates from a vast search space in a short span of time is challenging. Herein, a combined density functional theory and machine learning (ML) approach has been implemented to predict suitable M1M2XT2-type MXene materials that can optimally anchor the polysulfide intermediates. By employing various ML algorithms, the trained XGBR model is found to exhibit remarkable precision in predicting the anchoring effect of MXenes. 42 promising candidates have been identified to show optimum anchoring across the Al–S intermediates. The F and O terminal groups are found to majorly exhibit the optimum anchoring effect toward the possible polysulfide intermediates. This work provides crucial insights into the development of next-generation Al–S batteries accelerated by the ML approach, contributing to the advancement of energy storage technologies

Ga and Zn Atom-Doped CuAl₂O₄(111) Surface-Catalyzed CO₂ Conversion to Dimethyl Ether: Importance of Acidic Sites

Reduction of CO2 to high-value oxygenates is a desirable technique to store alternative renewable energy. In this regard, CO2 reduction to dimethyl ether (DME) utilizing bifunctional catalysts is important. Using density functional theory calculations, we have modeled the CuAl2O4(111) surface for CO2 conversion to DME and also checked the importance of dopants (Ga and Zn) in tuning the active center of the catalyst. The plausible reaction mechanism as well as free energy changes for both the steps, CO2 conversion to methanol, followed by dehydration of methanol to DME, has been determined. From the studies, the role of Ga in modifying the acidic site of the CuAl2O4(111) surface that directly facilitates methanol conversion to DME is highlighted. The synergistic effect of Ga and Cu atoms by changing the mode of intermediate adsorption is also noted for the efficient conversion of CO2 to DME. The activity of the catalyst in the presence of H2O has also been checked. Overall, this work signifies the importance of moderate acidic sites in improving the activity of the catalyst toward direct conversion of CO2 to DME

Catalytic Hydrogenation of CO₂ by Manganese Complexes: Role of π‑Acceptor Ligands

We have carried out computational studies on the CO₂ hydrogenation reaction catalyzed by three different Mn-based complexes (<b>Mn1</b>, <b>Mn2</b>, and <b>Mn3</b>) to understand the role of σ-donor (PMe₃) and π-acceptor (CO) character of the ligands. Further, the role of a different set of σ-donor and π-acceptor ligands is studied as the studied CO (π-acceptor and PMe₃ (σ-donor) ligands have the differences not only in electronic properties but also in steric effects. Here, we find that the σ-donor ligands (PMe₃/PH₃) favor the hydride transfer, whereas the π-acceptor ligands (CO/PF₃) favor the heterolytic H₂-cleavege. The energy profile diagram shows that the hydride transfer is the rate-determining step when the CO₂ hydrogenation reaction is catalyzed by a Mn-complex containing σ-donor (PMe₃/PH₃) and π-acceptor (CO/PF₃) ligands

Pt₃Ti (Ti₁₉@Pt₆₀)‑Based Cuboctahedral Core–Shell Nanocluster Favors a Direct over Indirect Oxygen Reduction Reaction

Developing a highly efficient catalyst with lower Pt content for the oxygen reduction reaction (ORR) is highly sought for fuel cell applications. The potential applicability of a cuboctahedral core–shell (Ti₁₉@Pt₆₀) nanocluster (NC) toward ORR activity has been investigated and compared with that of a pure Pt NC (Pt₇₉). The energetic stability, thermal stability, and dissolution limit of Ti₁₉@Pt₆₀ NCs has been investigated for their possible synthesis and practical usages. Thermodynamic and kinetic parameters are explored to find out the most favored ORR pathway and product selectivity on the Ti₁₉@Pt₆₀ NC. Rate-determining steps (*O₂ activation and *OH formation) are highly improved over the Ti₁₉@Pt₆₀ NC with respect to the cuboctahedral Pt NC (Pt₇₉), pure metal (Pt, Pd, and Ag), and alloy (Pt₃M; M = Ni, Co, Ti) based catalysts. Our detailed investigation reveals that the *O₂-induced structural changes favor direct *O₂ dissociation on the Ti₁₉@Pt₆₀ NC surface. Further, we find that a dual mechanism (ligand effect and charge transfer) plays an important role to improve the ORR activity. The results obtained in this study provide fundamental insight into the role of a core–shell NC toward ORR activity