Sensitivity to small changes. by Ariel Burman (20329776)

Morphological Degradation of Oxygen Evolution Reaction-Electrocatalyzing Nickel Selenides at Industrially Relevant Current Densities by Felix Hiege (21700895)

Morphological Degradation of Oxygen Evolution Reaction-Electrocatalyzing Nickel Selenides at Industrially Relevant Current Densities by Felix Hiege (21700895)

Morphological Degradation of Oxygen Evolution Reaction-Electrocatalyzing Nickel Selenides at Industrially Relevant Current Densities by Felix Hiege (21700895)

Volitional control frequency and intensity in VH (Kapsner-Smith et al., 2025) by Mara R. Kapsner-Smith (22139315)

“…Singers produced significantly smaller mean smallest changes of both <i>F</i>0 and intensity than nonsingers.…”

Microplastics Influence Dissolved Organic Matter Transformation Mediated by Microbiomes in Soil Aggregates by Xinran Qiu (9182255)

“…In this process, microbial communities play a significant role. They tend to consume DOM in larger aggregates and produce DOM in smaller aggregates, leading to an accumulation of DOM in smaller aggregates, thereby promoting the formation of smaller aggregates and reducing the aggregate stability. …”

Pond consolidation in the northwest Prairie Pothole Region changes water boatman (Hemiptera: Corixidae) species composition and reduces potential contribution to seasonal mass-migr... by Alexander Lapierre (22478656)

“…<p dir="ltr">Ongoing consolidation of numerous, smaller, non-permanent ponds into fewer, larger, permanent ponds in the Prairie Pothole Region of North America raises concern for species and ecosystem processes that may depend on clusters of small, non-permanent ponds. …”

Study group background characteristics. by Zicheng Hu (3825313)

“…Other intergroup comparisons were not significant. Overall, the albumin ratio elevations were small, and likely to have had only limited, if any, impact on the CSF proteins measured in this panel. …”

Supplementary file 1_Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm.pdf by Sanglin Zhao (20915732)

“…The results show that: (1) From 2000 to 2035, China’s total carbon emissions increased year by year, but the growth rate of carbon emissions gradually decreased; The carbon emission structure is “secondary industry > residents’ life > tertiary industry > primary industry”, and the growth rate of carbon in secondary industry and residents’ life is faster, while the change trend of primary industry and tertiary industry is smaller; (2) The spatial distribution of carbon emissions in China’s provinces presents a typical pattern of “eastern > central > western” and “northern > southern”, and the carbon emission centers tend to move to the northwest; (3) The regions with high level of digital economy, advanced industrial structure and new quality productivity have relatively less carbon emissions, which has significant group difference effect; (4) The intensity effect of energy consumption is the main factor driving the continuous growth of carbon emissions, while the per capita GDP and the structure effect of energy consumption are the main factors restraining carbon emissions, while the effects of industrial structure and population size are relatively small. …”