One-step dimethyl ether (DME) synthesis from CO2/H2 was investigated using physical mixtures of Hf-promoted CuZnO catalysts and FER. Methanol synthesis, the first step of DME production, was also studied using the same Cu-based catalysts. The catalysts were characterized by XRD with Rietveld refinement, XRF, specific area analysis, CO2-TPD, NH3-TPD, DRIFTS, N2O-RFC, and H2O dissociation measurements to evaluate oxygen vacancies. DFT calculations and thermodynamic analyses were also performed. Catalytic tests for methanol and DME synthesis under different reaction conditions, together with TG analysis, were conducted to investigate catalyst deactivation. Hf promotion significantly enhances catalytic performance by increasing both specific area and oxygen vacancy concentration through the substitution of Zn2⁺ by Hf4⁺ in the ZnO lattice. Consequently, methanol and DME yields increase up to an optimum Hf loading of 4 at.%, while the metallic Cu surface area remains essentially unchanged for Hf contents below 7 at.%. DFT calculations show that Hf substitution favors the spontaneous formation of oxygen vacancies in defective ZnO nanoparticles and on ZnO crystal surfaces. At higher Hf loadings, HfO2 formation blocks oxygen vacancies, active sites, and pores, leading to lower DME yield. Methanol synthesis reversibility under reaction conditions plays a decisive role in catalytic performance, favouring methanol and DME formation while suppressing CO. The proximity between methanol synthesis and dehydration sites enhances this effect. Furthermore, the acidity of the dehydration catalyst is an important factor controlling the DME formation rate. Catalyst deactivation is mainly attributed to H2O-promoted Cu sintering. Although CO is not an intermediate in methanol or DME synthesis, at high residence times CO produced by the reverse water–gas shift reaction can be re-adsorbed and hydrogenated to methanol and DME, allowing the system to approach thermodynamic equilibrium.
This paper presents a mixed-integer linear programming (MILP) model for optimizing surplus hydropower sales in Brazil's Free Contracting Market (ACL) under operational constraints and market volatility. The proposed framework integrates (i) dynamic liquidity constraints calibrated from historical trading activity, (ii) multi-horizon energy contracts (daily, monthly, quarterly, and semiannual), and (iii) risk management through Conditional Value at Risk (CVaR). Unlike related portfolio and contract-optimization models that typically neglect execution limits or adopt static volume caps, our formulation embeds time-and product-dependent liquidity envelopes and minimum-lot constraints in a single MILP with CVaR-based risk control. Computational experiments using 2000 price scenarios quantify the trade-off between expected revenue and tail-risk mitigation across different values of the risk-aversion parameter lambda.
This study investigates the effects of differentiated heritage protection regimes on townhouse property values in central Rio de Janeiro, contributing original evidence to a largely unexplored field in Brazil. Based on a sample of 86 townhouses, it applies a traditional hedonic pricing model (OLS), a Geographically Weighted Regression (GWR), and a Multiscale Geographically Weighted Regression with Spatial Autoregressive Error (MGWRSAR), which account for spatial heterogeneity and residual autocorrelation. To overcome data limitations, the analysis employs a bootstrap procedure to improve the robustness of inference. Results indicate that only properties under the least restrictive protection (“monitored”) display statistically significant price premiums. However, rather than reflecting a simple inverse relationship between restrictions and market value, this finding points to the importance of regulatory legibility, enforcement consistency, and perceived investment risks. The discussion highlights how flexible and intelligible protection regimes may foster market confidence, while ambiguous or overly rigid frameworks can undermine the effectiveness of heritage policy. The study also underscores a structural barrier to evidence-based policymaking: the lack of public access to transaction-level data in Brazil. This constraint limits empirical evaluation and transparency, both in academic research and in public administration.
Background Obese patients are at risk for chronic kidney disease (CKD). The high prevalence of hypertension and diabetes is central to the pathogenesis of kidney disease in these patients. In addition, specific factors such as age, central obesity, and the duration of obesity may also play a role. The present study examined the effects of these factors on the development of renal disease in a population of obese patients regularly followed in an outpatient program. Methods Obese patients in classes II–III were recruited from an obesity treatment program. Patients were grouped into CKD and non-CKD. Age, HbA1c, duration of obesity, urine albumin-to-creatinine ratio (uACR), and anthropometric data, including body weight, waist circumference, neck circumference, BMI, a body shape index (ABSI), and conicity index (CI), were correlated with eGFR. Odds ratios for CKD were examined for hypertension and diabetes, both crude and adjusted for age, gender, and race. Results Among 202 patients, the prevalence of CKD was 19.8%. Body weight, waist circumference, neck circumference, and BMI were not correlated with renal function. However, age, HbA1c, uACR, duration of obesity, and central obesity parameters, such as ABSI and CI, were significantly and inversely correlated with eGFR, while HbA1c levels were positively correlated with uACR. The odds ratio for CKD was 12.3 for hypertensives and 4.0 for diabetics, adjusted for age, race, and gender. Conclusions In this population of obese patients, systemic arterial hypertension confers the highest risk for CKD, followed by diabetes. Central obesity, as measured by ABSI and CI, was inversely correlated with renal function.
Plastic waste remains a major challenge for sustainable polymer science, requiring solutions that align with circular economy principles. This review provides a structured critical synthesis of comparative life cycle assessment (LCA) studies on waste management strategies, including mechanical recycling (MR), chemical recycling (CR), pyrolysis (PY), energy recovery (ER), incineration (IN), and landfill disposal (LF). Results show that MR was most frequently associated with lower impacts for clean polymer streams, cutting greenhouse gas emissions by up to 70% relative to virgin production. CR and PY were often reported as relevant options for mixed or contaminated plastics, though they remain energy-intensive with global warming potentials 30–50% higher than MR. ER and IN reduce volumes but often offset benefits through additional emissions, while LF was frequently ranked among the least favorable options. Beyond benchmarking, we identify persistent methodological gaps across LCAs, including variable system boundaries, omission of microplastic impacts, and limited techno-economic integration. Notably, 88% of studies emphasize global warming, whereas fewer than 50% address eutrophication or human health, constraining comparability and policy relevance. By consolidating fragmented evidence across waste streams and treatment routes, this review maps recurring methodological patterns and environmental trends relevant to future LCA studies and plastic waste management strategies.
