Effective treatment of coal slime water is essential for sustainable coal preparation plant operation but hindered by the stable suspension of fine, negatively charged particles. To address this, a novel star-shaped inorganic–organic hybrid polymer (aluminum hydroxide-polyacrylamide, Al-PAM) was synthesized via in situ polymerization. Its performance was systematically compared with well-established coagulants/flocculants—polyaluminum chloride (PAC), non-ionic polyacrylamide (NPAM), and their binary combination through settling tests and quartz crystal microbalance with dissipation monitoring (QCM-D). The results showed a positive correlation between the molecular weight of Al-PAM and its flocculation efficiency. The optimal variant, Al-PAM-442, achieved an exceptionally high initial settling rate (50.4 m/h) and low supernatant turbidity (45.77 NTU) at an ultralow dosage of 6 mg/L. QCM-D analysis elucidated the mechanism: Al-PAM forms a thick, soft, and irreversibly adsorbed hydrated layer on silica, enabling strong electrostatic anchoring and effective polymer bridging. In contrast, PAC adsorption was reversible, while NPAM formed a thin, compact film with poor bridging capacity. Although the combined PAC/NPAM system showed synergistic performance, it required a significantly higher dosage (70 mg/L). This study demonstrates that the star-shaped Al-PAM architecture successfully integrates charge neutralization and bridging into a single molecule, offering a highly efficient and practical solution for industrial coal slurry dewatering.

Microbial biosurfactants, derived from diverse aquatic and extreme ecosystems, offer a sustainable and environmentally compatible strategy for enhanced oil recovery by fundamentally altering subsurface rock wettability. These biologically produced amphiphiles can efficiently transform oil-wet rock surfaces into water-wet states, thereby mobilizing otherwise trapped crude oil. The primary aim of this review is to provide an integrative understanding of how these biomolecules function at the interface between aquatic microbial ecology and subsurface petroleum engineering, with a particular focus on wettability alteration as a key mechanism for enhancing oil recovery. This review surveys major biosurfactant classes—glycolipids, lipopeptides, and polymeric bioemulsifiers—and their core mechanisms, emphasizing their relevance to challenging reservoir conditions such as high temperature and salinity. A detailed assessment is devoted to persistent hurdles such as stability, adsorption onto rock formations, and economic scalability. Future prospects center on three key approaches: advancing synergistic “bio-hybrid” systems that integrate biosurfactants with complementary agents such as biopolymers and nanomaterials; achieving cost-effective production through the valorization of waste feedstocks; and expanding targeted bioprospecting of microbial diversity from extreme aquatic environments. Together, these strategies are reviewed to drive the advancement of robust, green microbial-enhanced oil recovery (MEOR) technologies, charting a course from fundamental insights to field-scale implementation.

High-ash coal slime water, characterized by its stable colloidal suspension of fine kaolinite particles, poses a significant challenge in the coal preparation industry because it is hard to achieve efficient solid–liquid separation. While traditional coagulants and flocculants often suffer from limited bridging capabilities and distinct pH sensitivity, novel molecular architectures offer potential solutions. In this study, a star-shaped inorganic–organic hybrid flocculant (Al-PAM) was synthesized via in situ polymerization. Its flocculation performance and interfacial adsorption mechanism on the specifically targeted aluminol basal plane of kaolinite were systematically investigated and compared with Polyaluminum Chloride (PAC), Non-ionic Polyacrylamide (NPAM), and their combination (PAC + NPAM). Settling tests revealed that Al-PAM exhibited superior performance at a significantly lower dosage (10 mg∙L⁻¹) compared to the PAC + NPAM binary reagent system. It achieved a rapid initial settling velocity and reduced the supernatant turbidity to 48.45 NTU, while maintaining a near-neutral pH favorable for water recycling. Furthermore, Quartz Crystal Microbalance with Dissipation (QCM-D) monitoring confirmed that Al-PAM forms a thick, viscoelastic, and irreversible adsorption layer on the Al₂O₃ substrate. The dissipation shifts (ΔD) revealed that the star-shaped architecture promotes distinct bridging and electrostatic adsorption, overcoming the limitation of linear polymers. This work elucidates the specific contribution of the alumina-surface interaction with flocculants and proposes an efficient strategy for treating refractory coal slime water.

Intelligent dosing in coal slime water treatment remains a challenge due to the lack of real-time and solid hardware-based measurement of key microscopic parameters governing the settling process, particularly zeta potential. This study proposes a soft-sensor method using Sparrow Search Algorithm-optimized Extreme Learning Machine (SSA-ELM) to simultaneously predict four critical settling process parameters: settling velocity, supernatant turbidity, sediment layer height, and zeta potential. Key variables influencing the coal slime water settling process, including coal slime water concentration, fines content, water hardness, pH, and chemical dosage, were investigated, and the experimental data were used as inputs for the development of the prediction model. The prediction performance of the proposed SSA-ELM model was evaluated against standard ELM and SSA-optimized Back Propagation (BP) models. The results demonstrate that the SSA-ELM model achieved superior prediction accuracy for all parameters, with R² values ranging from 0.95 to 0.98, while maintaining favorable computational efficiency. This study establishes a method for virtual measurement of zeta potential, providing a crucial data foundation for developing mechanism-driven, intelligent dosing systems aimed at precise intelligent control and reduced chemical consumption for coal preparation plants.

Microbial biosurfactants, derived from diverse aquatic and extreme ecosystems, offer a sustainable and environmentally compatible strategy for enhanced oil recovery by fundamentally altering subsurface rock wettability. These biologically produced amphiphiles can efficiently transform oil-wet rock surfaces into water-wet states, thereby mobilizing otherwise trapped crude oil. This review delineates their core mechanisms: wettability alteration, interfacial tension reduction, and emulsification. Major classes of biosurfactants, including glycolipids, lipopeptides, and polymeric bioemulsifiers are surveyed, focusing particularly on their relevance to challenging reservoir conditions. A detailed assessment is devoted to persistent hurdles such as stability under high temperature and salinity, adsorption onto rock formations, and economic scalability. Future prospects center on three key approaches: advancing synergistic “bio-hybrid” systems that integrate biosurfactants with complementary agents such as biopolymers and nanomaterials; achieving cost-effective production through the valorization of waste feedstocks; and expanding targeted bioprospecting of microbial diversity from extreme aquatic environments. Together, these strategies will drive the advancement of robust, green microbial enhanced oil recovery (MEOR) technologies.

Fueled by the push for “More than Moore”, three-dimensional integrated circuits (3D ICs) have become a backbone of next-generation electronics. Their complex architectures place unprecedented demands on etching technologies, which must now deliver atomic precision, stringent high-aspect-ratio (HAR) control, and virtually damage-free profiles. Meeting these challenges requires metrology capable of true 3D, quantitative analysis at the nanoscale. Atomic force microscopy (AFM) has proven essential in this regard, offering non-destructive, sub-nanometer characterization that other techniques cannot provide. This review systematically examines AFM’s pivotal role in advancing key etching processes for 3D ICs, including deep reactive ion etching of through-silicon vias (TSVs), atomic layer etching (ALE), and cryogenic plasma etching. We detail AFM’s unique contributions to quantifying sidewall roughness, verifying etch-per-cycle rates, and assessing surface damage. We also discuss how recent innovations, such as tilting-AFM, HAR probes, and automated inline systems, are overcoming traditional barriers in throughput and access to sidewalls and deep trenches. Looking forward, the integration of AFM with optical metrology, machine learning, and multi-scale modeling opens a path toward truly autonomous process control and optimization. As such, AFM stands as an indispensable tool for developing and refining the etching processes that underpin next-generation 3D semiconductor manufacturing.