Transforming low-ash coal slime into electromagnetic wave-absorbing materials can not only meet the growing demand for electromagnetic protection but also holds significant importance for promoting the transformation and upgrading of the coal industry. In this study, a series of cobalt ferrite/coal slime-based carbon composite absorbing materials were successfully prepared by loading cobalt ferrite nanoparticles onto a coal slime-based carbon substrate via a co-precipitation method. The results demonstrate that the composite exhibits optimal wave-absorption performance when the cobalt ferrite loading amount is 12.52 wt%. At a matching thickness of 3.5 mm, the minimum reflection loss reaches -32.08 dB at 12.73 GHz, with an effective absorption bandwidth as high as 8.84 GHz (covering 9.16-18.00 GHz). The microwave absorption mechanism of the composite is primarily attributed to magnetic loss induced by cobalt ferrite, including natural resonance and eddy current loss, as well as dielectric loss caused by coal slime-based carbon, involving interfacial polarization and dipole polarization. The effective combination of coal slime-based carbon and cobalt ferrite significantly optimizes the impedance matching characteristics of the composite. This research provides theoretical guidance for the high-value utilization of low-ash coal slime in the field of electromagnetic wave absorption.
Developing biodegradable and functional polymeric materials for active food packaging is essential to mitigate the environmental burden of petroleum-based plastics. In this context, gelatin/chitosan (G-Ch) nanofibrous mats were fabricated via solution blow spinning (SBS) and functionalized with snail slime (SS) and Pinus sylvestris essential oil (PSEO) to enhance their bioactivity and barrier performance. SS is rich in glycoproteins and natural bioactive compounds, while PSEO is characterized by terpene-based antimicrobial and antioxidant activities. SS and PSEO were incorporated into the G-Ch polymeric matrix to enhance the bioactivity, structural functionality and preservation performance of the nanofibrous mats. Three formulations (G-Ch, G-Ch-SS, and G-Ch-SS-10PSEO) were designed to elucidate the influence of snail slime and essential oil incorporation on the structure-property-function relationships of the nanofibrous mats. Morphological analysis revealed a smooth and bead-free fibrous structure across all formulations. The average fiber diameter (AFD) increased from 191.83 nm for G-Ch to 263.88 nm for G-Ch-SS and 295.83 nm for G-Ch-SS-10PSEO. FTIR and XRD analyses showed the physical encapsulation of the active compounds without significant chemical interactions. Furthermore, the incorporation of PSEO increased surface hydrophobicity and reduced air permeability, indicating the formation of a more compact fibrous structure with enhanced barrier properties. The functional performance of the nanofibrous mats was significantly improved by the addition of snail slime and PSEO. The G-Ch-SS-10PSEO formulation exhibited the highest antioxidant activity, reaching 36.8% for DPPH and 42.7% for ABTS, along with enhanced antibacterial efficacy against both Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus). Application tests on chicken wings demonstrated that the bioactive nanofibers effectively suppressed microbial growth, limited pH increases, and reduced lipid oxidation during 14 days of refrigerated storage. Overall, the results demonstrate that the synergistic integration of snail slime and essential oil within a biodegradable polymer matrix provides a promising strategy for designing active nanofibrous materials with enhanced structural and bioactive properties for sustainable food-packaging applications.
Pulmonary drug delivery is a promising approach for treating respiratory diseases, but the mucus barrier in bronchial airways often hinders therapeutic efficacy. The nanoscale structure of mucus is critical for drug penetration, yet remains poorly understood. In this study, we quantitatively characterize the structure of patient-derived pulmonary mucus and evaluate cross-linked snail slime as a structurally relevant biomimetic model. Using high-pressure freezing, cryosubstitution, and transmission electron microscopy with automated image analysis, we reveal that pulmonary mucus exhibits a fine, interconnected fibrous network with pore sizes below 500 nm, challenging prior reports of micrometer-scale pores. Applying the same methodology to cross-linked snail slime, we demonstrate that it faithfully replicates the nanoscale architecture of pulmonary mucus, beyond its rheological similarity. This work clarifies the structural organization of mucus and establishes cross-linked snail slime as a robust model for studying drug diffusion. Our detailed protocol also provides a reproducible framework for future investigations.
Slime moulds (Eumycetozoa) inhabit moisture-buffered microhabitats such as bark, dead wood, bryophyte mats and litter. Their assemblages are expected to track fine-scale forest structure and may therefore provide tractable signals for bioindication of forest microhabitat conditions, which represent local components of forest ecosystem condition. However, their potential as substrate-associated forest bioindicators has rarely been evaluated using harmonised data and study designs that account for sampling effort. A taxonomically standardised, georeferenced occurrence archive spanning forests in Central and Eastern Europe was used to quantify substrate affinities of slime moulds. The archive was also used to assess their suitability as ecological indicators of forest substrate (microhabitat) classes. Records were assigned to a consolidated 10-class substrate scheme representing forest substrate microhabitats where substrate descriptors were available. Analyses were restricted to these substrate (microhabitat) classes, which provided the analytical framework for all indicator assessments. After sample- size- and coverage-standardisation of substrate-level record counts, record-based diversity and evenness remained high and similar across substrates. However, assemblage composition differed consistently among corticolous (bark), lignicolous (dead wood), bryophilous (bryophyte mats) and other substrates. Effort-offset multi-species modelling quantified substrate-level contrasts, whereas effort-weighted IndVal statistics identified candidate indicator taxa, with the clearest signals for lignicolous and corticolous material. Species showed and interpretable elevational modes. In contrast, pH-associated patterns were evaluated using structured screening and should be interpreted cautiously. This screening nevertheless helps to delineate preliminary pH associations and to prioritise systematic co-measurement. Effort-aware analyses supported consistent, substrate-associated differences in assemblage composition among forest substrate (microhabitat) classes, whereas sample-size- and coverage-standardised diversity and evenness remained broadly similar across substrates. The clearest indicator signals were recovered for lignicolous and corticolous material, with Cribraria piriformis, Amaurochaete atra and Arcyria ferruginea characterising lignicolous (dead wood) substrates and Paradiacheopsis and Calomyxa characterising corticolous (bark) substrates. Accordingly, substrate-resolved Eumycetozoa occurrences can inform effort-aware, regionally stratified bioassessment of forest microhabitat conditions in European forests. Given the near-absence of measured pH values in the archive, pH associations remain preliminary and systematic co-measurement of pH is a priority. Improved coverage of under-represented substrates is also required to strengthen operational Eumycetozoa-based bioindication.
We tested whether ergot alkaloids would affect the interaction of a protist with an ergot alkaloid-producing fungus by studying the response of the plasmodial slime mold Physarum polycephalum to cultures of Aspergillus leporis. Ergot alkaloid profiles were manipulated by culturing A. leporis and its easD knockout derivative on media for 6 or 13 days. Six-day-old, wild-type A. leporis samples contained abundant lysergic acid α-hydroxyethylamide (LAH) that was greatly depleted by 13 days. The intermediate chanoclavine-I was the predominant ergot alkaloid in all samples of the easD knockout. Inoculum of P. polycephalum was placed equidistant between the fungus-colonized agar medium explant, and the preference of the slime mold for either fungal explant was observed. When offered 6-day-old samples of wild-type A. leporis (containing mainly LAH) or the easD knockout (containing mainly chanoclavine-I), plasmodia of P. polycephalum preferred the easD knockout strain over wild type by a significant margin (p = 0.0002). When given the same options at 13 days, there was no longer a preference for the easD knockout over wild type, which by that time had lost more than 90% of its LAH. Our data demonstrate that ergot alkaloids, including LAH and chanoclavine-I, affect the interaction of P. polycephalum with A. leporis.
To quantify recorded and potential (model-based) occurrence of slime moulds (Eumycetozoa) in Poland within a harmonised Central and Eastern European (CEE) framework, and to provide a reproducible basis for targeted survey planning and biodiversity assessment. Poland, with a comparative regional context based on harmonised occurrence data from 16 countries in Central and Eastern Europe. We used a taxonomically standardised, georeferenced Darwin Core dataset of Eumycetozoa compiled for 16 CEE countries. A Polish national checklist was assembled and annotated with habitat, substrate and national bibliographic information. We delineated an additional set of candidate taxa from the wider CEE pool and, where data permitted, evaluated them using presence-only species distribution models to estimate broad-scale climatic suitability in Poland and to identify regions and taxonomic groups concentrating potential diversity. The Polish checklist comprised 278 species in six orders, strongly dominated by Physarales, Trichiales, Stemonitidales and Cribrariales, with known records spatially clustered in a limited set of well-surveyed regions. Comparison with the regional pool indicated 365 additional candidate taxa in 11 orders not yet recorded from Poland. Of these, 101 species could be modelled, and 31 achieved medium or high model-based suitability within Poland. Adding these 31 taxa to the recorded checklist yields a conservative estimate of 309 species likely to occur nationally, whereas including all candidates provides a theoretical regional maximum of 643 taxa. Model predictions highlighted western and northern voivodeships as concentrating suitable climatic conditions for multiple candidate species. Harmonised occurrence data, an annotated national checklist and model-based predictions together indicate substantial survey gaps and a considerable pool of likely yet unrecorded taxa in Poland. These outputs provide a transparent, reproducible framework to prioritise future fieldwork, fill spatial and taxonomic gaps, and support the development of slime moulds as biodiversity indicators for forests in Poland, with regional climatic priorities to be validated against forest type, substrate availability and survey-time weather.
To address uncertainties regarding the environmental fate and functionality of alteration residues derived from coal slime-based activated materials (insoluble alteration residues) after soil application, this study investigated their immobilization capacity and atomic-scale mechanisms toward Pb2+ and Cd2+ using adsorption experiments, multiscale characterization, and density functional theory (DFT) calculations. The alteration product comprises muscovite and amorphous aluminosilicate phases with abundant exposed edge-site functional groups. Adsorption of both metals follows a spontaneous monolayer chemisorption process. Pb2+ shows a higher adsorption capacity and a faster adsorption rate than Cd2+. Mechanistic analyses reveal distinct fixation pathways: Pb2+ removal involves surface precipitation (PbCO3 and lead silicates) and strong inner-sphere complexation, while CdCO3 precipitation with weaker complexation dominates Cd2+ immobilization. DFT calculations further demonstrate that Pb binds more strongly than Cd on both crystalline edge surfaces and amorphous phases. These findings clarify the initial immobilization potential of coal slime-based material alteration products for heavy metals, providing a theoretical basis for assessing their environmental effects and for developing solid-waste-derived passivation materials.
Immunodeficiency significantly compromises host defense mechanisms and contributes to various pathologies. Monopterus albus whole slime (MS) is an underutilized aquatic by-product rich in proteins. This study examined the therapeutic effects and underlying mechanisms of MS and its purified protein (MSP) against cyclophosphamide (CTX)-induced immunodeficiency in mice. The results revealed that MSP significantly increased body weight, immune organ indices (spleen and thymus), cellular immune parameters (including counts of WBC, PLT, lymphocyte, and granulocyte), and humoral immune markers (including serum levels of IFN-γ, IL-2, and IgA) in immunocompromised mice. Furthermore, MSP demonstrated superior efficacy compared to MS in promoting thymic recovery and enhancing IgA production (p < 0.05). Concurrently, MSP ameliorated intestinal integrity through improved villus structure, upregulation of tight junction proteins (ZO-1 and occludin), attenuation of oxidative stress (evidenced by decreased MDA level and increased SOD and GSH-Px activities), and elevated secretory IgA (SIgA) levels. Gut microbiota analysis indicated that MSP promoted the enrichment of beneficial bacterial genera (norank_f__Muribaculaceae, Muribaculum) while suppressing pathogenic bacteria (Desulfovibrio, Lachnospiraceae_UCG-006, Eubacterium_xylanophilum_group). Fecal metabolomic analysis revealed that both MS and MSP altered the profiles of various metabolites, with enriched pathways involved in nucleotide metabolism, ABC transporters, and taurine and hypotaurine metabolism. Collectively, these findings suggest that MSP may mitigate CTX-induced immunodeficiency through a potential "gut microbiota-metabolite-intestinal barrier" axis, thereby establishes the theoretical groundwork for the development of slime-derived proteins as potential immunomodulatory agents.
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.
Estimating the pollution trend of biological resources such as soil is an important factor in environmental management. The present descriptive-applied research aimed to model and predict the amounts of total petroleum hydrocarbons (TPHs) in the soil of Ahvaz Operation Unit 1 using the Slime Mold Algorithm (SMA) in 2024. Data from the period 2012 to 2021 were analyzed by collecting 30 soil samples using a systematic grid sampling method. TPHs, including aliphatic and polycyclic aromatic hydrocarbons (PAHs), were measured by gas chromatography (GC). Ten-year climate data were used to increase prediction accuracy. The steps of implementing the SMA were defined using mathematical optimization terminology, including adaptive exploration, exploitation of the search space, and convergence. The findings showed that the average of the highest amount of PAH compounds was 2980.5 μg/kg, with the highest contamination reported for Chr and B.a.a (5542.5 and 3684.3 μg/kg, respectively). Also, the average of total aliphatic compounds was 3649 mg/kg. There was a statistically significant spatial difference between the concentrations among different sampling stations (p < 0.05). Based on the normalized dataset, the SMA model showed that the RMSE for aromatics and aliphatics were 0.135 and 0.148, indicating higher accuracy in predicting aromatic compounds. The coefficient of determination (R2) and adjusted were 0.72 and 0.68 for aromatics, and 0.65 and 0.60 for aliphatics, respectively. The results indicate that the SMA shows strong potential for wider use in modeling soil contamination and offers a practical framework for environmental pollution management, although further validation across different climatic regions is recommended.
Manganese is essential for zinc electrowinning but its recycling is challenging in oxide ore SX-EW circuits where P204 rejects Mn. This study proposes a novel manganese recycling strategy tailored for SX-EW streams involving the selective SO2 reductive leaching of zinc anode slime followed by impurity removal. By controlling leaching pH at 5.0 with lime, Mn recovery > 92% while Fe, Ni, Co co-dissolution is suppressed to < 1%, 3%, 12% respectively. Residual impurities in the spent electrolyte are further removed by neutralization and DMDTC chelation. This integrated process effectively recovers manganese while simultaneously eliminating harmful impurities (Cu, Fe, Co, Ni), offering a sustainable solution for Mn management in complex oxide ore hydrometallurgy. The purified electrolyte enabled production of Grade #1 zinc with Pb < 0.005%.
Co-pelletization of municipal sewage sludge (SS) and coal slime (CS) offers a promising route for waste to fuel conversion, but its practical application is often limited by insufficient pellet cohesion and poor mechanical stability. In this study, three bio-derived solid waste binders, namely waste paper (WP), rice straw (RS), and lignin (CL), were incorporated at 10-20 wt% into a 1:1 SS/CS blend to evaluate their effects on pellet quality, combustion behavior, and formulation suitability. The results showed that all three binders improved pellet performance, although through different pathways. CL provided the strongest mechanical reinforcement, and the 20% addition achieved the highest densification and Meyer hardness (approximately 5.9-6.0 MPa), whereas WP showed the greatest improvement in combustion performance. At a 20% addition level, the comprehensive combustion index (S) reached 3.49 × 10-7·%2 min-2 °C-3, which was about 2.99 times that of the control. Kinetic reconstruction based on thermogravimetric analysis indicated that the apparent activation energy varied only slightly among formulations, suggesting that the combustion differences were governed mainly by structural evolution and the associated heat/mass transfer conditions rather than by fundamental changes in reaction barrier. To avoid selecting formulations solely on the basis of combustion metrics, techno-economic assessment (TEA) and ash fusion temperature (AFT) were further incorporated into the screening framework. Although 20% WP showed the highest combustion reactivity, 15% WP provided the best overall balance, with the maximum net profit and low slagging risk, as indicated by softening temperatures above 1330 °C. These results demonstrate that the optimal SS-CS pellet formulation is determined by the combined balance among mechanical stability, combustion reactivity, economic return, and ash-related risk.
The efficient recovery of platinum group metals (PGMs) from decoppered anode slimes is essential for sustainable resource management, yet the atomic-level mechanisms underlying their capture remain unclear. Herein, first-principles calculations were employed to elucidate the microscopic interactions by which bismuth acts as a trapping agent for PGMs (Ru, Ir, Pt, Rh, Os, Pd) and to determine the effects of four representative impurities (As, Sb, Pb, Si). The results demonstrate that pristine Bi(001) exhibits strong chemisorption toward all six PGMs, as proved by the large charge transfer, significant electron sharing and pronounced p-d orbital hybridization. Furthermore, these impurities spontaneously incorporate into the Bi(001) surface due to the large binding energy. Crucially, some impurities such as As and Si function as potent surface activators rather than detrimental contaminants. These dopants significantly enhance the PGM binding strength by inducing intense localized charge redistribution and establishing strong orbital hybridizations among the Bi-5d, PGM-d and p orbitals of dopants. Overall, this work provides a theoretical foundation for strategically utilizing the impurities to optimize the recovery of PGMs in complex smelting systems.
Early detection is critical to improving outcomes across many diseases, yet cliniciansmust rapidly interpret heterogeneous signals, reports, and images. Automated analysis helps uncover subtle patterns and anomalies that may elude human review. This work targets real-time clinical decision support by streaming data from a portable Internet-of-Things multi sensor device to the cloud and applying a hybrid optimizer classifier pipeline for robust diagnosis. We implemented a Slime Mould Algorithm tuned Support Vector Machine (SMA-SVM) with data preprocessing and feature selection via backward elimination, then partitioned the dataset into training and test sets for evaluation. In comparative experiments, the proposed SMA-SVM outperformed established baselines including SVM, LSTM, DNN, RNN, and PSO-CNN achieving improvements of 16.11%, 16.69%, 7.79%, 11.46%, and 1.75%, respectively, for cardiovascular disease diagnosis. These results indicate that metaheuristic tuning coupled with classical margins can deliver fast, accurate, and resource-efficient predictions suitable for continuous monitoring settings.
Spontaneous alternation behavior (SAB) is a robust paradigm to investigate short-term spatial memory across diverse taxa. While extensively studied in animals, its presence in unicellular aneural organisms remains poorly understood. Here, we tested SAB in the slime mold Physarum polycephalum using 3D-printed T-mazes with forced turns at varying distances (3 mm, 7 mm, and 14 mm), as well as in a double-turn design. A total of 1274 plasmodia from a clonal line were examined under controlled laboratory conditions. Our results reveal significant alternation behavior only in the short-distance maze (3 mm), independent of turn direction. Neither medium nor long distances, nor the double-turn de-sign, yielded significant effects after correction for multiple testing. These findings suggest that SAB in Physarum depends on spatial scale, with decision-making localized to the active moving front of the plasmodium. It is yet unclear if the observed behavior is induced by the topography of the used mazes or, as in other organisms, a result of memory. Further study on possible mechanisms guiding this behavior are required.
This research work focuses on optimizing outer and inner PI controllers by employing Slime Mould Algorithm (SMA) and Ant Colony Optimization (ACO) techniques for precise speed control of a PMBLDC drive which finds applications in electric vehicles. Power from the solar photovoltaic system is processed by the hybrid DC-DC converter and subsequently delivered to the voltage source inverter to drive PMBLDC drive. State space modeling of the hybrid DC-DC converter is developed using small-signal modeling, and the transfer function is derived using the state space averaging technique. Owing to the presence of five energy storage elements, the derived converter transfer function is of fifth order. To reduce the system complexity, model order reduction using the Hankel matrix is applied to obtain a third-order system. A closed loop control scheme is implemented using ACO tuned and SMA tuned outer and inner PI controllers. The system performance is assessed under line, load, and set point variations for both optimization techniques. Simulation results demonstrate that the SMA tuned outer and inner PI controllers deliver superior performance, achieving the desired motor speed with reduced time domain specifications under all operating conditions. To validate the simulation results, a 240-W experimental prototype controlled by a dsPIC30F2010 is developed. Simulation Results reveal that the SMA-tuned PI controller offers improved dynamic performance with reduced time domain specifications. and achieves an efficiency of 91.61%.
During deep and ultra-deep oil and gas drilling, downhole high-temperature and high-pressure conditions significantly affect the measurement accuracy of piezoresistive pressure sensors. To improve measurement accuracy under such extreme conditions, this study proposes an intelligent temperature compensation method based on a Modified Slime Mold Algorithm (MSMA). An experimental platform covering the full operating range of 0-175 °C and 0-170 MPa was established to acquire sensor outputs, and samples were collected at various temperature and pressure points to construct a dataset. Key parameters of the compensation model were optimized using the MSMA, enhancing the model's fitting capability. Results indicate that, after compensation, the sensor exhibits a maximum full-scale error of 0.26% and a maximum sensitivity drift of -0.019% FS/°C, significantly reducing errors compared with traditional interpolation and polynomial fitting methods. The optimized compensation model was further deployed on an embedded hardware platform, enabling high-precision temperature compensation in an engineering context. Experimental data demonstrate that the embedded implementation maintains compensation accuracy while meeting real-time application requirements, making it suitable for downhole pressure monitoring and for output correction of other intelligent sensors operating under complex field conditions.
Inspired by cellular slime molds, a biomimetic self-supporting MXene membrane is fabricated. This design integrates covalent cross-linking for stability with phosphate groups for specific uranium capture. The membrane achieves a high extraction capacity of 3776.25 mg g-1 under an applied voltage, demonstrating a promising strategy for advanced nuclear wastewater treatment.
Biological transport networks achieve efficient transport while maintaining functionality under environmental changes and damage. Among them, the true slime mold Physarum polycephalum has attracted attention as it adaptively reorganizes its network structure based solely on local interactions. The Physarum model is known to be equivalent to optimal transport (OT) and has been applied in transport network studies. However, implementing it in artificial networks requires solving a graph-Laplacian linear system in a centralized manner, which makes local and distributed execution difficult in applications such as logistics systems and limits the exploitation of the environmental robustness inherent in slime mold behavior. Accordingly, we propose distributed Physarum-OT, a model that reflects the local and distributed nature of real slime mold. This method is formulated as a bilevel optimization problem wherein slime mold network development and the solution of the linear system are treated as the outer and inner problems, respectively. Furthermore, we propose a computational relaxation method with convergence guarantees. Numerical experiments on a logistics network show that the proposed method converges to OT in a locally distributed manner while adapting smoothly to cost changes in a slime-mold-like manner.
Coal slime water treatment and resource recovery are vital for the sustainable development of coal industry sustainability. Kaolinite, over 60% of clay minerals in coal slime water, is key for high-value flotation utilization. Phosphonic-acid collectors adsorb effectively on kaolinite via -PO(OH)2 groups, but their structural diversity (phenyl/benzylphosphonic acids and esters) blurs structure-adsorption relationships. Existing studies focus on single collectors for specific minerals, lack a systematic screening/prediction database, and rarely combine first-principle calculations with three-dimensional quantitative structure-activity relationship (3D-QSAR) to explore multi-type collector mechanisms on kaolinite. This study combined density functional theory (DFT) with 3D-QSAR to study phosphonic-acid collector adsorption on kaolinite (001). Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models were validated, with CoMSIA performing optimally (q2 = 0.843, r2 = 0.984). Diethyl (2-chlorobenzyl)phosphonate and (2-hydroxyphenyl)phosphonic acid in the test set showed prediction errors < 1%, confirming reliability. Two novel collectors (4-propylphenylphosphonic acid, 3-methyl-4-nitrophenylphosphonic acid) were designed, outperforming all database collectors, corroborating model validity and supporting high-efficiency collector development for kaolinite recovery. First-principle calculations via Cambridge Serial Total Energy Package (CASTEP) yielded the adsorption energies of 35 phenyl/benzylphosphonic acids/esters on kaolinite (001) to build a molecular structure-adsorption database. The dataset was split into 80% training and 20% test sets post molecular energy minimization. CoMFA/CoMSIA models were built via partial least squares (PLS) regression, evaluated by q2, r2, F-statistic and standard error of estimate (SEE); contour maps analyzed molecular field effects. New collectors were designed via CoMSIA and DFT-verified.