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1. Optically pumped deep-UV multimode lasing in AlGaN double heterostructure grown by molecular beam homoepitaxy

   https://doi.org/10.1063/5.0085365  

   van Deurzen, Len ; Page, Ryan ; Protasenko, Vladimir ; Nomoto, Kazuki ; Xing, Huili ; Jena, Debdeep ( March 2022 , AIP Advances)

   Multimode lasing at sub-300 nm wavelengths is demonstrated by optical pumping in AlGaN heterostructures grown on single-crystal AlN substrates by plasma-assisted molecular beam epitaxy. Edge-emitting ridge-based Fabry–Pérot cavities are fabricated with the epitaxial AlN/AlGaN double heterostructure by a combined inductively coupled plasma reactive ion etch and tetramethylammonium hydroxide etch. The emitters exhibit peak gain at 284 nm and modal linewidths on the order of 0.1 nm at room temperature. The applied growth technique and its chemical and heterostructural design characteristics offer certain unique capabilities toward further development of electrically injected AlGaN laser diodes. 

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2. Mechanistic Understanding on Difluoromethane Absorption Thermodynamics on Novel Deep Eutectic Solvents by COSMO-Based Molecular Simulation

   https://doi.org/10.3390/app13106182  

   Quaid, Thomas ; Reza, M. Toufiq ( May 2023 , Applied Sciences)

   Hydrofluorocarbons (HFC) are fluorinated compounds used globally for refrigeration. These gases have been shown to contain a greenhouse potential of up to 22,000 times that of CO2. Thus, 1298 type-5 deep eutectic solvents (DES) were examined for the absorption and interaction mechanisms of difluoromethane (R32), due to their non-polar attributes. Of these solvents, quaternary ammonium salts mixed with various species of hydrogen bond donators (HBD) produced the most favorable interactions, with ln activity coefficients predicted to be as low as −1.39 at 1:1 compositional ratio. These DES were further studied for compositional analysis where pure tetrabutylammonium bromide showed the strongest interaction potential. The pressure study showed a linear solubility increase with a pressure increase reaching up to 86 mol/mol% in a methyltrioctylammonium bromide and polyethylene glycol mixture at 9 bar. The van der Waals interaction is the driving force of absorption with ~3x enthalpic release over hydrogen bonding. All chemicals contain strong potential for an environmentally friendly solution, as is evident through an environmental health and safety analysis.

    

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3. ALMA SPECTROSCOPIC SURVEY IN THE HUBBLE ULTRA DEEP FIELD: CO LUMINOSITY FUNCTIONS AND THE EVOLUTION OF THE COSMIC DENSITY OF MOLECULAR GAS

   https://doi.org/10.3847/1538-4357/833/1/69  

   Decarli, Roberto ; Walter, Fabian ; Aravena, Manuel ; Carilli, Chris ; Bouwens, Rychard ; da Cunha, Elisabete ; Daddi, Emanuele ; Ivison, R. J. ; Popping, Gergö ; Riechers, Dominik ; et al ( December 2016 , The Astrophysical Journal)
4. The ALMA Spectroscopic Survey in the Hubble Ultra Deep Field: Constraining the Molecular Content at log( M * / M ⊙ ) ∼ 9.5 with CO Stacking of MUSE-detected z ∼ 1.5 Galaxies

   https://doi.org/10.3847/1538-4357/abba2f  

   Inami, Hanae ; Decarli, Roberto ; Walter, Fabian ; Weiss, Axel ; Carilli, Chris ; Aravena, Manuel ; Boogaard, Leindert ; Gonzaĺez-López, Jorge ; Popping, Gergö ; da Cunha, Elisabete ; et al ( October 2020 , The Astrophysical Journal)
5. A generalized deep learning approach for local structure identification in molecular simulations

   https://doi.org/10.1039/c9sc02097g  

   DeFever, Ryan S. ; Targonski, Colin ; Hall, Steven W. ; Smith, Melissa C. ; Sarupria, Sapna ( January 2019 , Chemical Science)

   Identifying local structure in molecular simulations is of utmost importance. The most common existing approach to identify local structure is to calculate some geometrical quantity referred to as an order parameter. In simple cases order parameters are physically intuitive and trivial to develop ( e.g. , ion-pair distance), however in most cases, order parameter development becomes a much more difficult endeavor ( e.g. , crystal structure identification). Using ideas from computer vision, we adapt a specific type of neural network called a PointNet to identify local structural environments in molecular simulations. A primary challenge in applying machine learning techniques to simulation is selecting the appropriate input features. This challenge is system-specific and requires significant human input and intuition. In contrast, our approach is a generic framework that requires no system-specific feature engineering and operates on the raw output of the simulations, i.e. , atomic positions. We demonstrate the method on crystal structure identification in Lennard-Jones (four different phases), water (eight different phases), and mesophase (six different phases) systems. The method achieves as high as 99.5% accuracy in crystal structure identification. The method is applicable to heterogeneous nucleation and it can even predict the crystal phases of atoms near external interfaces. We demonstrate the versatility of our approach by using our method to identify surface hydrophobicity based solely upon positions and orientations of surrounding water molecules. Our results suggest the approach will be broadly applicable to many types of local structure in simulations. 

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