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Iodinated disinfection by-products (I-DBPs) are of growing concern due to their elevated toxicity compared to their chlorinated counterparts, with links to adverse health effects such as bladder cancer and miscarriages. Medical imaging agents like iohexol, commonly used in healthcare facilities, introduce iodine into wastewater systems. This study investigates the photodegradation of iohexol and the subsequent formation of products, including I-DBPs, during simulated final wastewater treatment under chlorination and sunlight exposure. Experiments were conducted with solutions containing 30 μM iohexol, 3 mg L−1 humic acids, and 5.5 mg L−1 hypochlorite. Samples were irradiated at λ ≥ 295 nm and subject to ion chromatography monitoring of I−, IO3−, Cl−, and ClO3−, providing mechanistic insight into the fate of iodide released from iohexol. UV-visible spectroscopy was employed to monitor the degradation profile of iohexol and the concurrent release of iodide. Electrospray ionization mass spectrometry (ESI-MS) identified a range of anionic products based on their mass-to-charge ratios (m/z), including low molecular weight carboxylic acids, their carcinogenic haloacetic derivatives (chloroacetic acid (m/z 93), iodoacetic acid (IAA, m/z 185), and hydroxyiodoacetic acid (m/z 201)) as well as phenolic halides. Notably, IAA was present at a concentration of 0.16 μM at the conclusion of the reaction. These findings elucidate photodeiodination-coupled radical attack, photooxidative cleavage, and halogenation transformation pathways of iodinated compounds during disinfection and underscore the potential risks associated with their presence in wastewater. The results provide valuable insights for medical facilities and wastewater treatment plants aiming to mitigate the formation of hazardous I-DBPs. 

Many industries are keenly interested in detecting and classifying faults before systems are sent to the customer or fail in use. A common approach is measuring the vibration of the machine and then using a classifier to check whether a fault is present. However, this process is difficult to automate because accelerometers are applied to the unit under test and are sometimes difficult to install and maintain due to complicated surface conditions. Accurate contact-based sensing is difficult when trying to check each rotating machinery assembly product during end-of-line quality control examinations or when evaluating the machine health of pre-installed rotating machinery. A deep learning-based fault classification system using both scalar and vector acoustic signals is a promising alternative that can replace the traditional error-prone, contact-based methods. Acoustic sound pressure and particle velocity measurements capture the directional fault signature of the mechanical defects in electric motors, and a one-dimensional convolutional neural networks (1D-CNNs) approach is proposed to process raw sensing data and eliminate the need for manual feature extraction. An experimental case study is performed to test the proposed 1D-CNN based fault classification on three different mechanically faulty electric motors across a variety of speeds. The results from acoustic pressure and particle velocity signals are compared against those from accelerometer signals. The experimental study confirms the feasibility of the proposed 1D-CNN on acoustic signals to be an excellent replacement for contact-based methods when assessing and classifying the machine fault condition. © 2025 Institute of Noise Control Engineering. 

The synthesis of ultrathin WS₂nanosheets and HAADF-STEM image highlighting the defect areas (edge defects and S line vacancies). 

Abstract In equivariant algebra, Mackey functors play the role of abelian groups and Green and Tambara functors play the role of commutative rings. In this paper, we compute Mackey functor‐valued Tor over certain free Green and Tambara functors, generalizing the computation of Tor over a polynomial ring on one generator. In contrast with the classical situation where the resulting Tor groups vanish above degree one, we present examples where Tor is nonvanishing in almost every degree. 

Not AvailableTwo-dimensional halide perovskites (2D-HPs) are of significant interest for their applications in optoelectronic devices. Part of this increased interest in 2D-HPs stems from their increased stability relative to their 3D counterparts. Here, the origin of higher stability in 2D-HPs is mainly attributed to the bulky ammonium cation layers, which can act as a blocking layer against moisture and oxygen ingression and ion diffusion. While 2D-HPs have demonstrated increased stability, it is not clear how the structure of the ammonium ion impacts the material stability. Herein, we investigate how the structure of ammonium cations, including three n-alkyl ammoniums, phenethylammonium (PEA) and five PEA derivatives, anilinium (An), benzylammonium (BzA), and cyclohexylmethyl ammonium (CHMA), affects the crystal structure and air, water, and oxygen stability of 2D tin halide perovskites (2D-SnHPs). We find that stability is influenced by several factors, including the molecular packing and intermolecular interactions in the organic layer, steric effects around the ammonium group, the orientation distribution of the 2D sheets, and the hydrophobicity of the perovskite film surface. With superior hydrophobicity, strong interactions between organic layers, and a high extent of parallel oriented inorganic sheets, the 2-(4-trifluoromethyl-phenyl)-ethylammonium (4-TFMPEA) ion forms the most stable 2D-SnHP among the 12 ammonium cations investigated. 

While wide bandgap (WBG) switches have revolutionized power electronics and motor-drive systems, the high dv/dt associated with these fast-switching semiconductors can easily induce reflected high-frequency overvoltage spikes on motor stator terminals. The shorter rise time of the voltage pulses confines the cable length between the inverter and the motor in practice to avoid overvoltage across the motor stator windings. Even with shorter cables, voltage spikes from variable-speed drives can still cause premature insulation failure and reduce the remaining useful lifetime of the motors. While effective, conventional methods such as dv/dt passive filters or active gate drivers are usually bulky and/or inefficient. To address this problem, an overvoltage mitigation solution, named “Smart Coil,” is introduced in this article. The smart coil circuit is installed in parallel with the first coil of each motor phase, which typically experiences the highest reflected overvoltage. Upon detection of overvoltage, the proposed ultracompact smart coil circuit, located at the motor junction box, is activated to limit voltage stress across the coils. Since the smart coil is connected in parallel with the first coil, it only needs to process very low pulsed power during the overvoltage transients. Therefore, it has high efficiency and an ultracompact footprint while effectively mitigating voltage spikes. The proposed smart coil circuit can be easily scaled for various motor-drive systems regardless of the cable length or rise time of the switching devices. Simulation and experimental test results are provided to verify the effectiveness of the proposed method. 

Abstract The use of γ‐Al₂O₃‐supported Ni catalysts promoted with either Cu or Fe was investigated for the reductive catalytic fractionation (RCF) of hybrid poplar in methanol at 200 and 250 °C. The effectiveness of lignin depolymerization was quantified in terms of the lignin oil production, the quantity and distribution of identifiable monomers present in the lignin oil, and the yield of residual solids. All of the Ni‐based catalysts tested provided improved yields of lignin oil and monomers, along with reduced char formation, relative to blank (sans catalyst) runs. The highest monomer yield of 51 % was obtained at 250 °C over a 20 wt.% Ni‐5 wt.% Cu/Al₂O₃catalyst, the improved performance obtained through Cu promotion being attributed to the ability of Cu to facilitate NiO reduction, resulting in an increased amount of Ni⁰on the catalyst surface and, consequently, improved hydrogenation activity. The main monomers formed were propanol‐, propyl‐ and propenyl‐substituted guaiacol and syringol, the S/G ratio of the products corresponding closely to that in the native lignin. 

Abstract BackgroundUndergraduate engineering students experiencing distress are less likely than peers to ask for professional help. A population‐specific instrument to facilitate the identification of factors that influence mental healthcare utilization could guide development and testing of interventions to increase help seeking. PurposeWe used mixed methods guided by the Integrated Behavioral Model (IBM) to develop and evaluate the Undergraduate Engineering Mental Health Help‐Seeking Instrument (UE‐MH‐HSI). MethodFirst, we adapted existing measures of mental health help‐seeking intention and mechanisms (i.e., attitudes, perceived norm: injunctive, perceived norm: descriptive, personal agency: autonomy, personal agency: capacity). Second, we coded qualitative interviews (N = 33) to create population‐specific mental health help‐seeking belief measures (i.e., outcome beliefs, experiential beliefs, beliefs about others' expectations, beliefs about others' behavior, beliefs about barriers and facilitators). Third, we tested the psychometric properties using data from 596 undergraduate engineering students at a historically White, research‐focused institution in southern United States. ResultsPsychometric analyses indicated that (1) help‐seeking mechanism and intention measures demonstrated unidimensionality, internal consistency, construct replicability, and sufficient variability; (2) mechanism measures demonstrated criterion evidence of validity; and (3) most items within the belief measures demonstrated sufficient variability and convergent evidence of validity. ConclusionsThe UE‐MH‐HSI is an evidence‐based tool for investigating mental health help‐seeking factors and their relationship to help‐seeking behavior, well‐being, academic success, and engineering identity formation. Guidelines for use are provided. 

The estrous cycle regulates rhythms of locomotor activity, body temperature, and circadian gene expression. In female mice, activity increases on the night of proestrus, when elevated estrogens cause ovulation. Exogenous estradiol regulates eating behavior rhythms in female mice fed a high-fat diet, but it is unknown whether endogenous estrogens regulate eating rhythms. In this study, we investigated whether diurnal and circadian eating behavior rhythms change systematically across the estrous cycle. We first studied diurnal eating behavior rhythms in female C57BL/6J mice in 12L:12D. Estrous cycle stages were determined by vaginal cytology while eating behavior and wheel revolutions were continuously measured. The mice had regular 4- to 5-day estrous cycles. Consistent with prior studies, the greatest number of wheel revolutions occurred on the night of proestrus into estrus when systemic levels of estrogens peak. The amplitude, or robustness, of the eating behavior rhythm also fluctuated with 4- to 5-day cycles and peaked primarily during proestrus or estrus. The phases of eating behavior rhythms fluctuated, but not at 4- or 5-day intervals, and phases did not correlate with estrous cycle stages. After ovariectomy, the eating behavior rhythm amplitude fluctuated at irregular intervals. In constant darkness, the amplitude of the circadian eating behavior rhythm peaked every 4 or 5 days and coincided with the circadian day that had the greatest number of wheel revolutions, a marker of proestrus. These data suggest that fluctuations of ovarian hormones across the estrous cycle temporally organize the robustness of circadian eating behavior rhythms so that it peaks during ovulation and sexual receptivity.