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1. Identifying Alkali-Silica Reaction in Cementitious Materials Using Volatilomics

   https://doi.org/10.1007/978-3-031-59419-9_71  

   Ideker, Jason H; Crawford, Kelsey; Gustafson, Collin; Parashar, Anuj; Davis, Trenton J; Diefenderfer, Jenna; Bean, Heather D (May 2024, Springer Nature Switzerland)

   Sanchez, Leandro F; Trottier, Cassandra (Ed.)

   For millennia, the medical field has utilized the sense of smell for qualitative assessment of health, but recent research shows we can tap into volatile organic compounds (VOCs), which create the odors that we perceive, for quantitative detection and analysis. In this paper, volatile organic compounds produced by the microbes in aged concrete, actively undergoing deterioration due to alkali-silica reaction were analyzed. Volatile organic compounds metabolites, an oft unused resource of chemical information that are produced by concrete-associated microbial communities, were used to detect, and characterize concrete deterioration. In this talk, preliminary results of volatile detection on long-term samples (e.g., ~ 7 years) will be provided. Scanning electron microscopy with energy disperse x-ray analysis was used to confirm deterioration mechanisms identified using volatilomics. Volatiles were analyzed using direct thermal extraction (DTE) and comprehensive two-dimensional gas chromatography - time-of-flight mass spectrometry (GC × GC–TOFMS). Identifying VOC biomarkers of concrete health will lay the groundwork for the development of sensors that can provide early deterioration warning, enabling more effective remediation/repair strategies. Microbes may also be sensitive to environmental stress (e.g., climate change), and a sensor based on microbial volatile organic compounds could be used to monitor infrastructure health and provide data to detect environmental stressors relevant to other fields. 

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2. Morphology of pig nasal structure and modulation of airflow and basic thermal conditioning

   https://doi.org/10.1093/icb/icad005  

   Yuk, Jisoo; Akash, Mohammad Mehedi Hasan; Chakraborty, Aneek; Basu, Saikat; Chamorro, Leonardo P.; Jung, Sunghwan (February 2023, Integrative And Comparative Biology)

   Abstract Mammals have presumably evolved to adapt to a diverse range of ambient environmental conditions through the optimized heat and mass exchange. One of the crucial biological structures for survivability is the nose, which efficiently transports and thermally preconditions the external air before reaching the internal body. Nasal mucosa and cavity help warm and humidify the inhaled air quickly. Despite its crucial role, the morphological features of mammal noses and their effect in modulating the momentum of the inhaled air, heat transfer dynamics, and particulate trapping remain poorly understood. Tortuosity of the nasal cavity in high-olfactory mammalian species, such as pigs and opossum, facilitates the formation of complex airflow patterns inside the nasal cavity, which leads to the screening of particulates from the inhaled air. We explored basic nasal features in anatomically realistic nasal pathways, including tortuosity, radius of curvature, and gap thickness; they show strong power-law correlations with body weight. Complementary inspection of tortuosity with idealized conduits reveals that this quantity is central in particle capture efficiency. Mechanistic insights into such nuances can serve as a tipping point to transforming nature-based designs into practical applications. In-depth characterization of the fluid–particle interactions in nasal cavities is necessary to uncover nose mechanistic functionalities. It is instrumental in developing new devices and filters in a number of engineering processes. 

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3. Quantized non-volatile nanomagnetic domain wall synapse based autoencoder for efficient unsupervised network anomaly detection

   https://doi.org/10.1088/2634-4386/ad49ce  

   Alam, Muhammad Sabbir; Misba, Walid Al; Atulasimha, Jayasimha (June 2024, Neuromorphic Computing and Engineering)

   Abstract Anomaly detection in real-time using autoencoders implemented on edge devices is exceedingly challenging due to limited hardware, energy, and computational resources. We show that these limitations can be addressed by designing an autoencoder with low-resolution non-volatile memory-based synapses and employing an effective quantized neural network learning algorithm. We further propose nanoscale ferromagnetic racetracks with engineered notches hosting magnetic domain walls (DW) as exemplary non-volatile memory-based autoencoder synapses, where limited state (5-state) synaptic weights are manipulated by spin orbit torque (SOT) current pulses to write different magnetoresistance states. The performance of anomaly detection of the proposed autoencoder model is evaluated on the NSL-KDD dataset. Limited resolution and DW device stochasticity aware training of the autoencoder is performed, which yields comparable anomaly detection performance to the autoencoder having floating-point precision weights. While the limited number of quantized states and the inherent stochastic nature of DW synaptic weights in nanoscale devices are typically known to negatively impact the performance, our hardware-aware training algorithm is shown to leverage these imperfect device characteristics to generate an improvement in anomaly detection accuracy (90.98%) compared to accuracy obtained with floating-point synaptic weights that are extremely memory intensive. Furthermore, our DW-based approach demonstrates a remarkable reduction of at least three orders of magnitude in weight updates during training compared to the floating-point approach, implying significant reduction in operation energy for our method. This work could stimulate the development of extremely energy efficient non-volatile multi-state synapse-based processors that can perform real-time training and inference on the edge with unsupervised data. 

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4. Emerging investigator series: deposited particles and human lung lining fluid are dynamic, chemically-complex reservoirs leading to thirdhand smoke emissions and exposure

   https://doi.org/10.1039/D1EA00107H  

   Sheu, Roger; Hass-Mitchell, Tori; Ringsdorf, Akima; Berkemeier, Thomas; Machesky, Jo; Edtbauer, Achim; Klüpfel, Thomas; Filippi, Alexander; Musa Bandowe, Benjamin A.; Wietzoreck, Marco; et al (June 2022, Environmental Science: Atmospheres)

   Thirdhand smoke (THS) persists in locations where smoking previously occurred and can be transported into non-smoking environments, leading to non-smoker exposure. Laboratory experiments using high-resolution mass spectrometry demonstrate that deposited particulate matter (PM) and smoke-exposed surrogate lung lining fluid (LLF) are substantial, chemically-complex reservoirs of gas-phase THS emissions, including hazardous air pollutants, polycyclic aromatic compounds, and nitrogen/oxygen-containing species. Both PM and LLF are persistent real-world THS reservoirs that chemically evolve over time, and can act as vehicles for the transport and emission of reactive pollutants and their reaction byproducts (e.g., acrolein). Deposited PM on clothes, furnishings, bodies, and/or airways will emit volatile to semi-volatile gases over long lifetimes, which can re-partition to other indoor materials and increase their overall persistence. On the other hand, LLF off-gassing consists predominantly of volatile organic compounds in amounts influenced by their aqueous solubilities, and their persistence in breath will be prolonged by re-distribution across internal aqueous reservoirs, as corroborated by multicompartment modeling in this study. 

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5. Physics-Informed Machine Learning of Argon Gas-Driven Melt Pool Dynamics

   https://doi.org/10.1115/1.4065457  

   Sharma, R; Guo, Y B; Raissi, M; Guo, W Grace (August 2024, Journal of Manufacturing Science and Engineering)

   Abstract Melt pool dynamics in metal additive manufacturing (AM) is critical to process stability, microstructure formation, and final properties of the printed materials. Physics-based simulation, including computational fluid dynamics (CFD), is the dominant approach to predict melt pool dynamics. However, the physics-based simulation approaches suffer from the inherent issue of very high computational cost. This paper provides a physics-informed machine learning method by integrating the conventional neural networks with the governing physical laws to predict the melt pool dynamics, such as temperature, velocity, and pressure, without using any training data on velocity and pressure. This approach avoids solving the nonlinear Navier–Stokes equation numerically, which significantly reduces the computational cost (if including the cost of velocity data generation). The difficult-to-determine parameters' values of the governing equations can also be inferred through data-driven discovery. In addition, the physics-informed neural network (PINN) architecture has been optimized for efficient model training. The data-efficient PINN model is attributed to the extra penalty by incorporating governing PDEs, initial conditions, and boundary conditions in the PINN model. 

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