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Research Paper#Condensed Matter Physics, 2D Materials, Excitons🔬 ResearchAnalyzed: Jan 3, 2026 16:47

Linear Exciton Hall and Nernst Effects in 2D Semiconductors

Published:Dec 30, 2025 11:36
1 min read
ArXiv

Analysis

This paper investigates the linear exciton Hall and Nernst effects in monolayer 2D semiconductors. It uses semi-classical transport theory to derive the exciton Berry curvature and analyzes its impact on the Hall and Nernst currents. The study highlights the role of material symmetry in inducing these effects, even without Berry curvature, and provides insights into the behavior of excitons in specific materials like TMDs and black phosphorus. The findings are relevant for understanding and potentially manipulating exciton transport in 2D materials for optoelectronic applications.
Reference

“The specific symmetry of 2D materials can induce a significant linear exciton Hall (Nernst) effect even without Berry curvature.”

PermalinkArXiv
Research Paper#Tribology, Lubrication, Machine Learning, Molecular Dynamics🔬 ResearchAnalyzed: Jan 3, 2026 16:03

Phosphorus Additives for Lubrication: A Machine Learning Study

Published:Dec 29, 2025 16:33
1 min read
ArXiv

Analysis

This paper uses machine learning to understand how different phosphorus-based lubricant additives affect friction and wear on iron surfaces. It's important because it provides atomistic-level insights into the mechanisms behind these additives, which can help in designing better lubricants. The study focuses on the impact of molecular structure on tribological performance, offering valuable information for optimizing additive design.
Reference

“DBHP exhibits the lowest friction and largest interfacial separation, resulting from steric hindrance and tribochemical reactivity.”

PermalinkArXiv
Research#llm👥 CommunityAnalyzed: Jan 4, 2026 10:36

Phospho – Text Analytics for LLM Apps (Posthog for Prompts)

Published:Mar 13, 2024 15:14
1 min read
Hacker News

Analysis

The article introduces Phospho, a tool for text analytics specifically designed for applications built on Large Language Models (LLMs). It positions itself as a 'Posthog for Prompts,' suggesting it provides similar functionality to Posthog but tailored for analyzing and understanding the performance of prompts within LLM applications. The focus is on providing insights into how prompts are performing, likely including metrics like success rates, error rates, and user engagement. The 'Show HN' format on Hacker News indicates it's a new product being presented to the tech community for feedback and potential adoption. The comparison to Posthog implies a focus on user behavior and data-driven optimization.
Reference

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PermalinkHacker News