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There has been a lot of attention on water pollution by dyes in recent years because of their serious toxicological implications on human health and the environment. Therefore, the current study presented a novel polyethylene glycol-functionalized graphene oxide/chitosan composite (PEG-GO/CS) to remove dyes from aqueous solutions. Several characterization techniques, such as SEM, TEM, FTIR, TGA/DTG, XRD, and XPS, were employed to correlate the structure–property relationship between the adsorption performance and PEG-GO/CS composites. Taguchi’s (L₂₅) approach was used to optimize the batch adsorption process variables [pH, contact time, adsorbent dose, and initial concentration of methyl orange (MO)] for maximal adsorption capacity. pH = 2, contact time = 90 min, adsorbent dose = 10 mg/10 mL, and MO initial concentration = 200 mg/L were found to be optimal. The material has a maximum adsorption capacity of 271 mg/g for MO at room temperature. With the greatest R² = 0.8930 values, the Langmuir isotherm model was shown to be the most appropriate. Compared to the pseudo-first-order model (R² = 0.9685), the pseudo-second-order model (R² = 0.9707) better fits the kinetic data. Electrostatic interactions were the dominant mechanism underlying MO sorption onto the PEG/GO-CS composite. The as-synthesized composite was reusable for up to three adsorption cycles. Thus, the PEG/GO-CS composite fabricated through a simple procedure may remove MO and other similar organic dyes in real contaminated water.

This paper presents a normalized standard error-based statistical data binning method, termed “bin size index” (BSI), which yields an optimized, objective bin size for constructing a rational histogram to facilitate subsequent deconvolution of multimodal datasets from materials characterization and hence the determination of the underlying probability density functions. Totally ten datasets, including four normally-distributed synthetic ones, three normally-distributed ones on the elasticity of rocks obtained by statistical nanoindentation, and three lognormally-distributed ones on the particle size distributions of flocculated clay suspensions, were used to illustrate the BSI’s concepts and algorithms. While results from the synthetic datasets prove the method’s accuracy and effectiveness, analyses of other real datasets from materials characterization and measurement further demonstrate its rationale, performance, and applicability to practical problems. The BSI method also enables determination of the number of modes via the comparative evaluation of the errors returned from different trial bin sizes. The accuracy and performance of the BSI method are further compared with other widely used binning methods, and the former yields the highest BSI and smallest normalized standard errors. This new method particularly penalizes the overfitting that tends to yield too many pseudo-modes via normalizing the errors by the number of modes hidden in the datasets, and also eliminates the difficulty in specifying criteria for acceptable values of the fitting errors. The advantages and disadvantages of the new method are also discussed.

Three studies introduce a novel individual-difference construct to explain majority group members’ responses to organizational diversity efforts: lay theories of diversity initiatives. Zero-sum beliefs (ZSBs) presume that efforts to advance diversity come at the expense of majority group members. Win-win beliefs (WWBs) posit that diversity initiatives can benefit all racial groups. Study 1 created and validated measures of ZSBs and WWBs. Study 2 showed that ZSBs and WWBs are distinct from 10 intergroup measures (e.g., social dominance orientation, modern racism) and that each lay theory accounts for unique variance in explaining Whites’ reactions to diversity initiatives. Study 3 demonstrated that ZSBs and WWBs predict Whites’ reactions to diversity policies of a hypothetical company. These findings suggest that ZSBs and WWBs both help explain majority group members’ backlash against and support for organizational diversity initiatives.