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Conventional black box machine learning (ML) algorithms for gas-phase species identification from THz frequency region absorption spectra have been reported in the literature. While the robust classification performance of such ML models is promising, the black box nature of these ML tools limits their interpretability and acceptance in application. Here, a one-dimensional convolutional neural network (CNN), VOC-Net, is developed and demonstrated for the classification of absorption spectra for volatile organic compounds (VOCs) in the THz frequency range, specifically from 220 to 330 GHz where prior experimental data is available. VOC-Net is trained and validated against simulated spectra, and also demonstrated and tested against experimental spectra. The performance of VOC-Net is examined by the consideration of confusion matrices and receiver-operator-characteristic (ROC) curves. The model is shown to be 99+% accurate for the classification of simulated spectra and 97% accurate for the classification of noisy experimental spectra. The model’s internal logic is examined using the Gradient-weighted Class Activation Mapping (Grad-CAM) method, which provides a visual and interpretable explanation of the model’s decision making process with respect to the important distinguishing spectral features. 

Gas sensing for dimethyl sulfoxide (DMSO) based on rotational absorption spectroscopy is demonstrated in the 220–330 GHz frequency range using a robust electronic THz-wave spectrometer. DMSO is a flammable liquid commonly used as a solvent in the food and pharmaceutical industries, materials synthesis, and manufacturing. DMSO is a hazard to human health and the work environment; hence, remote gas sensing for DMSO environmental and process monitoring is desired. Absorption measurements were carried out for pure DMSO at 297 K and 0.4 Torr (53 Pa). DMSO was shown to have a unique rotational fingerprint with a series of repeating absorption bands. The frequencies of transitions observed in the present study were found to be in good agreement with spectral simulations carried out based on rotational parameters derived in prior work. Newly, intensities of the rotational absorption lines were experimentally observed and reported for DMSO in this study. Measured intensities for major absorption lines were found in very good agreement with relative line intensities estimated by quantum mechanical calculations. The sensor developed here exhibited a detection limit of 1.3 × 1015–2.6 × 1015 DMSO molecules/cm3 per meter of absorption path length, with the potential for greater sensitivity with signal-to-noise improvements. The study illustrates the potential of all electronic THz-wave systems for miniaturized remote gas sensors. 

Body size covaries with population dynamics across life’s domains. Metabolism may impose fundamental constraints on the coevolution of size and demography, but experimental tests of the causal links remain elusive. We leverage a 60,000-generation experiment in which Escherichia coli populations evolved larger cells to examine intraspecific metabolic scaling and correlations with demographic parameters. Over the course of their evolution, the cells have roughly doubled in size relative to their ancestors. These larger cells have metabolic rates that are absolutely higher, but relative to their size, they are lower. Metabolic theory successfully predicted the relations between size, metabolism, and maximum population density, including support for Damuth’s law of energy equivalence, such that populations of larger cells achieved lower maximum densities but higher maximum biomasses than populations of smaller cells. The scaling of metabolism with cell size thus predicted the scaling of size with maximum population density. In stark contrast to standard theory, however, populations of larger cells grew faster than those of smaller cells, contradicting the fundamental and intuitive assumption that the costs of building new individuals should scale directly with their size. The finding that the costs of production can be decoupled from size necessitates a reevaluation of the evolutionary drivers and ecological consequences of biological size more generally.