Search for: All records

Abstract The valorization of waste‐derived feedstocks for polymer synthesis represents a sustainable alternative to petroleum‐based materials. In this study, brown grease, a low‐value waste lipid source, is utilized as a precursor for polyol monomer synthesis via a two‐step functionalization process. Transesterification of brown grease with allyl alcohol generates allyl esters, which are subsequently modified via thiol‐ene click chemistry with 2‐mercaptoethanol to yield hydroxyl‐functionalized polyols (BG‐diol). The thiol‐ene reaction proceeds under mild UV‐initiated conditions, achieving high conversion efficiency (>90%) while preserving the structural integrity of the derived polyol.BG‐diolis further polymerized with 4,4′‐methylene diphenyl diisocyanate (MDI) through step‐growth polymerization to form brown grease‐derived polyurethane (BG‐PU). Comparative analysis ofBG‐PUwith polyurethane (PU) synthesized from purified oleic acid (OLA‐PU) demonstrates comparable molecular weight distributions (M_n = 14.4 kDa,M_w = 20.4 kDa forBG‐PU) and thermal properties (T_g = 24 °C,T_d,5%= 270 °C forBG‐PU). These results underscore the feasibility of brown grease as a cost‐effective and renewable alternative to plant oil‐based polyols, offering a pathway toward sustainable PU production while mitigating food security concerns. This approach exemplifies the potential of waste lipids in circular economy strategies for high‐performance polymer synthesis. 

ABSTRACT High sulfur‐content materials (HSMs) prepared via inverse vulcanization are attractive for a range of sustainable material applications, particularly when synthesized from waste‐derived feedstocks such as brown grease (BG). Two BG‐based composites,SunBG₉₀andaBG₉₀, were prepared using elemental sulfur and either native or allylated brown grease, respectively. This study explores the effect of reinforcing these sulfur‐rich networks with low loadings (0.5–2 wt. %) of highcis‐1,4‐content liquid polybutadiene (PBD). Incorporation of PBD resulted in significant increases in storage modulus, with a near‐linear relationship between PBD content and stiffness enhancement for both material types. At −60°C, storage modulus increased more than fivefold foraBG₉₀and more than tripled forSunBG₉₀. In contrast, flexural strength and flexural modulus exhibited non‐linear responses, with diminishing or reversed gains at higher PBD loadings, suggesting limits to rubber domain compatibility and dispersion. Thermal analysis confirmed high decomposition temperatures (212°C–226°C) and stable glass transitions, indicating thermal robustness of the reinforced networks. Compared with previous studies requiring higher PBD loadings, these results demonstrate that BG‐based HSMs can be effectively reinforced at low additive levels, offering mechanically robust, low‐cost, and renewable alternatives for structural applications. 

Sequential crosslinking steps convert industrial waste sulfur into versatile high-sulfur content materials (HSMs), each with distinct properties, providing a tunable platform for advanced material development. 

Abstract As Deep Neural Networks (DNNs) are being increasingly employed to make important simulations in rainfall‐runoff contexts, the demand for interpretability is increasing in the hydrology community. Interpretability is not just a scientific question, but rather knowing where the models fall flat, how to fix them, and how to explain their outcomes to scientific communities so that everyone understands how the model arrives at specific simulations This paper addresses these challenges by deciphering interpretable probabilistic DNNs utilizing the Deep Autoregressive Recurrent (DeepAR) and Temporal Fusion Transformer (TFT) for daily streamflow simulation across the continental United States (CONUS). We benchmarked TFT and DeepAR against conceptual to physics‐based hydrologic models. In this setting, catchment physical attributes were incorporated into the training process to create physics‐guided TFT and DeepAR configurations. Our proposed physics‐guided configurations are also designed to aggregate the patterns across the entire data set, analyze the sensitivity of key catchment physical attributes and facilitate the interpretability of temporal dynamics in rainfall‐runoff generation mechanisms. To assess the uncertainty, the modeling configurations were coupled with a quantile regression by adding Gaussian noise with increasing standard deviation to the individual catchment attributes. Analysis suggested that the physics‐guided TFT was superior in predicting daily streamflow compared to the original TFT and DeepAR as well as benchmark hydrologic models. Predictive uncertainty intervals effectively bracketed most of the observational data by simultaneous simulation of various percentiles (e.g., 10th, 50th, and 90th). Interpretable physics‐guided TFT proved to be a strong candidate for CONUS daily streamflow simulations. 

Abstract Due to their diverse potential in advanced electronics and energy technologies, electrically conducting metal‐organic frameworks (MOFs) are drawing significant attention. Although hexagonal 2D MOFs generally display impressive electrical conductivity because of their dual in‐plane (through bonds) and out‐of‐plane (through π‐stacked ligands) charge transport pathways, notable differences between these two orthogonal conduction routes cause anisotropic conductivity and lower bulk conductivity. To address this issue, we have developed the first redox‐complementary dual‐ligand 2D MOF Cu₃(HHTP)(HHTQ), featuring a π‐donor hexahydroxytriphenylene (HHTP) ligand and a π‐acceptor hexahydroxytricycloquinazoline (HHTQ) ligand located at alternate corners of the hexagons, which form either parallel HHTP and HHTQ stacks (AA stacking) or alternating HHTP/HHTQ stacks (AB stacking) along the c‐axis. Regardless of the stacking pattern, Cu₃(HHTP)(HHTQ) supports more effective out‐of‐plane conduction through either separate π‐donor and π‐acceptor stacks or alternating π‐donor/acceptor stacks, while promoting in‐plane conduction through the pushpull‐like heteroleptic coordination network. As a result, Cu₃(HHTP)(HHTQ) exhibits higher bulk conductivity (0.12 S/m at 295 K) than single‐ligand MOFs Cu₃(HHTP)₂(7.3 × 10⁻²S/m) and Cu₃(HHTQ)₂(5.9 × 10⁻⁴S/m). This work introduces a new design approach to improve the bulk electrical conductivity of 2D MOFs by supporting charge transport in both in‐ and out‐of‐plane direcations. 

This study evaluates the use of post-consumer fast-food restaurant waste and elemental sulfur to create high-strength composite materials. Compressive strengths exceed those of C62 building brick and flexural strengths are competitive with OPC. 

ABSTRACT Brown grease (BG) is a high‐free fatty acid (FFA) waste coproduct from the food industry that remains largely unexploited. Herein, we describe a design strategy to upcycle BG into high sulfur‐content materials (HSMs) via inverse vulcanization, circumventing the need for costly transition metals or food‐grade compatibilizers. First, BG was esterified with methyl or allyl groups, yielding MeBG and aBG, respectively. This modification masked the polar carboxylic acids and enhanced miscibility with molten sulfur. Subsequent inverse vulcanization produced remeltable HSMs at 80 or 90 wt% sulfur with uniform elemental distributions by SEM–EDX. FT‐IR spectroscopy revealed the consumption of C=C moieties and the formation of C–S bonds, signifying robust cross‐linking. Thermal analysis (TGA, DSC) indicated good thermal stability (T_d,5%up to 223°C) and glass transitions characteristic of polysulfide networks. Mechanical evaluations demonstrated compressive strengths up to 19.2 MPa, exceeding the minimum requirement for residential foundation‐grade cement (17 MPa) and rivaling previously reported HSMs containing similarly high sulfur content. Notably, MeBG and aBG incorporate organics comprising up to 97 wt% BG, significantly improving the upcycled mass efficiency relative to earlier BG‐based composites. This esterification‐driven approach thus offers a practical, scalable pathway to convert low‐value BG into advanced materials with tunable thermomechanical properties. 

This exploratory sequential mixed-methods paper explores the relationship between gig and taxi drivers’ perceptions of autonomous vehicles (AVs) and their continuance intentions. Drawing from the Career Construction Model of Adaptation, we examined the relationship between drivers’ expectations about AV-related job changes and their intentions to stay or leave their driving role upon the integration of AVs. In Study 1, we collected qualitative data from gig and taxi drivers (N= 69) in 24 focus groups. In Study 2, we administered a survey to gig and taxi drivers (N= 496). The thematic analysis in Study 1 revealed how drivers expected the onset of AVs to positively and negatively impact their job (changes to work stress, safety, job enjoyment, etc.). These expectations influenced their decisions to remain in or leave their jobs. Multivariate regression in Study 2 showed that multiple factors identified in Study 1 were related to continuance intentions, with some being “retention factors” (related to intentions to stay) and others being “turnover factors” (related to intentions to leave). Our findings contribute to the evolving discourse on the impact of new technologies on continuance intentions by offering theoretical and practical implications in careers and vocational behavior. 

Post-polymerization modification with rigid aryl dithiols enables systematic control over the thermal and mechanical properties of guaiacol-derived high sulfur-content materials. 

Herein, we have synthesized high-performance, low-cost ionomer biocomposites comprised of sulfonated poly(ether ether ketone) (SPEEK) and softwood Kraft lignin with proton selectivities three- to ten-fold higher than that of neat SPEEK.