Magnetic nanosorbents efficiently capture substances, particularly basic dyes, and can be easily recovered using a magnetic field in water treatment. Adsorption is a cost-effective and highly efficient method for basic dye removal. This study compared eight nonlinear kinetic adsorption models using Microsoft Excel 2023, which provided a detailed analysis and statistical results comparable to advanced programs like MATLAB and OriginPro. The Fractal Like-Pseudo First Order (FL-PFO) model showed the best fit for the kinetic adsorption model, closely predicting experimental data at 33.09 mg g⁻¹. This suggests that the diffusion rate of basic dye within the magnetic nanosorbent pores is a crucial factor. The statistical parameters confirmed the suitability of these kinetic adsorption models for describing the observed behavior. Overall, Microsoft Excel emerged as a reliable tool for predicting adsorption behavior using various kinetic models for basic dye removal, offering a wide range of functions for diverse applications, including environmental monitoring and modeling. Corrected Akaike’s information criterion was used to determine the optimal model. It found the lowest AIC_corrected value of about −3.8479 for the FL-PFO kinetic model, while the Elovich kinetic adsorption model had the highest AIC_corrected value of 29.6605. This indicates that the FL-PFO kinetic model effectively correlated the kinetic data. It can be concluded that Microsoft Excel’s accessibility, familiarity, and broad range of capabilities make it a valuable resource for many aspects of environmental engineering.

We discuss background interference (BGI) arising from the read-back of preexisting data and adjacent tracks on a recording medium. A signal-to-noise ratio analysis is used to measure the impact of BGI in heat-assisted magnetic recording in a spin-stand tester. The signal-to-noise ratio for a data track with an alternating field background was compared to a data track written over a pair of background tracks. We considered the effect of background track offsets: the distance between the background and data tracks. BGI, which depends on the background track offset, can degrade read-back performance. The dibit extraction technique is able to separate the linear dibit response and the BGI in the read-back waveform.

Recently, heat-assisted magnetic recording (HAMR) has been demonstrated to extend the areal density growth over the superparamagnetic limited. One key component of this technology is a heat sink layer in HAMR media, which benefits the thermal gradient and transition noise. However, the disadvantage of the heat sink layer (HS) has not been fully explored. In this paper, we investigate the background interference (BGI) impact as a result of the heat sink layer via the spin-stand tester. HAMR heads included a light delivery system that have measured a signal-to-noise ratio and down-track thermal gradient on a variety of HS thickness. Subsequently, we found that a thicker HS is a trade-off between the BGI and thermal gradient. Thus, it remains challenging to achieve an ultra-high areal density using the thermal media design.

Several studies have confirmed that current-perpendicular-to-the-plane giant magnetoresistance (CPP-GMR) technology is appropriate for next-generation read sensors for ultrahigh areal densities (ADs) of data storage applications. Since the physical dimension of the read sensor is a crucial factor for developing the reader to overcome its limitations, this paper proposes an optimal sizing prediction of the CPP-GMR read heads for ADs of 1-4 Tb/in2. Micromagnetic modelling was performed in the simulations. The appropriate length of the stripe height (SH) and the read width (RW) of the readers was estimated based on a consideration of sensor outputs including the readback signal, asymmetry parameter, dibit response and power spectral density (PSD) profile. It was found that a variation of SH and RW lengths had an influential impact on the readback signal waveform. Those affectations were further characterized through the echoes of dibit response showing that shortening the SH length or increasing the RW length could improve the resolution and reduce the distortion occurring in the readback signal. Moreover, the PSD profile indicated that the reader operation became more stable at shorter SH lengths or longer RW lengths. The head response spectrum was also examined. In addition, the magnitude of the bias current was studied in relation to the head response. Lastly, the optimal physical dimension (SH×RW) of the CPP-GMR readers for ADs of 1-4 Tb/in2 was predicted to be (40×48) nm, (28×29) nm, (25×26) nm and (19×20) nm, respectively. The results can be utilized to design the CPP-GMR sensors at ultrahigh magnetic recording capacities.

Geological CO₂ storage requires compressibility assumptions suited to the effective-stress interval and loading path. We assess their adequacy by separating curve approximation, prediction, parameter identification, and storage consequence. An exact minimax bound tests when a constant logarithmic slope cannot meet a specified tolerance. Across 19 published sediment trajectories, full-data calibration of Arc Joint via Optimum Parameters (AJOP) gave a median range-normalized void-ratio RMSE of 1.27% and a maximum of 3.41% for its five-parameter form; 12 fits selected its exact linear member. The quadratic had a lower median error but locally reversed compression in four cases. Separate blocked tests, thirteen independent Boston Blue Clay specimens, and an intact Draupne record expose limits on prediction and transfer. In twelve archived Smeaheia scenarios, compressibility choices changed pressure-screen classifications. The assessment connects tests of simple assumptions, the value of added curvature, and calibration requirements to storage screening, while preserving the distinction between successful loading-curve approximation and field validation.

Pressure buildup in geological CO₂ storage depends on how pore volume changes with effective stress, yet the laboratory evidence behind simulator compressibility inputs is often sparse. We test when a constant logarithmic compression slope is adequate and what the choice changes in a caprock screen. An exact minimax bound identifies loading intervals where a constant slope must fail. Arc Joint via Optimum Parameters (AJOP) and five competitors were fitted to 19 published trajectories with trajectory-level statistics, information criteria and bootstrap tangent uncertainty, and an independent-specimen cohort was ranked before and after a pre-declared exclusion. Caprock laws based on two unloading moduli measured on one Draupne specimen were run in OPM Flow on a layered model and the published Smeaheia simulation model. AJOP5 gave a median range-normalized RMSE of 1.27% (95% CI 0.81–1.74%); the quadratic gave 0.26% but reversed in four cases. The curvature term was unsupported in 12 fits, and tangent-slope uncertainty reached 123%. High-stress rankings reversed after exclusion. The caprock law changed reservoir buildup by at most 19% but caprock pressure by up to 59% (layered model) and changed no screening class. The bound applies to primary loading only, and small curve errors do not ensure reliable tangent properties.