Precision Engineering Research Articles

Manipulating Near-Infrared Absorption via Engineering Anisotropic Plasmonic Spiky Au Nanocubes for the Highly Efficient Dual-Response Immune Detection of T-2 Toxin.

Integrating specific immune recognition, a desirable extinction coefficient, and conspicuous photothermal conversion ability into a single-immune probe to enhance the analysis performance represents an appealing yet significantly challenging task. Herein, by delicately manipulating the geometry of plasmonic nanoparticles from spherical to spiky, precise engineering approach-based spiky Au nanocubes (S-AuNCs) are employed to address this challenge, which fully exploits the plasmon resonance absorption-induced photothermal effect. The finite difference time domain (FDTD) method was employed to computationally simulate the electromagnetic and thermal fields while assessing the feasibility of regulating plasmon resonance for enhanced photothermal absorption. The optimized noble photothermal agent simultaneously exhibits acceptable near-infrared absorption (NIR), a significantly increased 808 nm extinction coefficient (145 times higher than that of AuNPs), favorable antibody coupling ability, and desirable photothermal conversion behavior. Consequently, the satisfactory performance of the S-AuNCs-guided colorimetric and photothermal lateral flow immunoassay (CPLFIA) is demonstrated for the sensitive detection of T-2 toxin. In comparison to spherical AuNPs (35.2 pg/mL), the dual-mode detection sensitivity was enhanced by 1.862-fold and 5.18-fold, respectively, achieving limits of detection at 18.9 pg/mL (colorimetric mode) and 6.8 pg/mL (photothermal mode). Therefore, S-AuNCs-guided CPLFIA holds great potential in advancing food mycotoxin safety control.

Tailoring Ga‐Doped ZnO Thin Film Properties for Enhanced Optoelectric Device Performance: Argon Flow Rate Modulation and Dynamic Sputtering Geometry Analysis

The impact of dynamic sputtering geometry on the properties of ZnO: Ga (GZO) thin film nanomaterials is investigated by systematically varying Ar flow rates and substrate positions during the film growth. The structural, optical, and electrical characteristics of GZO layers, deposited from a ZnO: Ga (5.7 wt%) ceramic‐type sputtering target, are comprehensively evaluated to reveal the relationship between the sputtering geometry and material properties. The obtained electrical properties, comparatively high carrier mobility 11.3 × 101 cm2 V−1 s−1 and the lowest resistivity 1.13 × 10−3 Ω‐cm, together with a moderately high optoelectric figure of merit with the films prepared using around 6 sccm Ar‐flow rate (corresponding to around 4.92 mTorr Ar partial pressure) reveal distinct correlations between the sputtering conditions and thin film properties, providing insights into the optimization of sputtering parameters for tailored material synthesis required for advanced and emerging applications. The GZO thin film (prepared with the optimal setting of 6 sccm Ar flow rate) exhibits remarkable optoelectronic capabilities as a transport layer in solar cells, reaching peak efficiencies of 26.34% for CIGS, 14.142% for CdTe, and 24.289% for Cs2AgBiBr6 perovskite in SCAPS‐1D simulated models. This study advances sputtering techniques for precise engineering of functional nanomaterials with enhanced performance and versatility, contributing to material synthesis optimization for emerging applications.

Mid‐Infrared on‐Chip Soliton Self‐frequency Shift in Chalcogenide Glass Waveguide

AbstractMid‐infrared soliton lasers leveraging the Raman self‐pumping induced soliton self‐frequency shift (SSFS) effect offer continuously tunable, highly efficient, femtosecond coherent sources that are essential for applications such as spectroscopy, metrology, and quantum optics. However, despite significant advancements in fluoride and chalcogenide fiber platforms, realizing mid‐infrared Raman soliton lasers on on‐chip platforms remains challenging. In this study, the first experimental demonstration of a mid‐infrared Raman soliton laser in an on‐chip Ge28Sb12Se60 (GeSbSe) chalcogenide glass waveguide is presented. A fully fiberized femtosecond fiber laser, centered at 1.96 µm and emitting 246 fs pulses at a 50 MHz repetition rate, is utilized as the pump source, establishing a fiber‐to‐chip configuration. The waveguides are meticulously fabricated using e‐beam lithography and plasma etching, achieving high optical quality and precision in the mid‐infrared regime. Through precise geometrical dispersion engineering, a Raman soliton laser is achieved that continuously tunes from 1960 to 2145 nm within a 32.5 mm long snakelike GeSbSe strip waveguide. The threshold for pump peak power is remarkably low, at just 14.1 W (3.47 pJ). Additionally, a more than one‐octave‐spanning near to mid‐infrared supercontinuum (1320–2760 nm at 22.9 pJ), reinforced by the combined Kerr and Raman effects, is also realized, confirming the versatile performance of the proposed GeSbSe waveguide. These findings pave the way for mid‐infrared on‐chip Raman soliton lasers, highlighting their potential for power‐efficient, low‐cost, and field‐deployable on‐chip applications in the mid‐infrared regime.

A Practical Approach to Modeling and Performance Analysis of a Turboshaft Engine Using Matlab

This article presents a detailed approach to constructing a numerical model of a free power turbine engine specifically designed for performance analysis in the Matlab R2024b environment. The core innovation of this model lies in its integration of precise engine geometry parameters, calculated at the design point, and performance characteristics of key components such as the compressor and power turbine. These components are modeled to reflect significant variations in their performance based on changing operational conditions, including rotor speed and environmental factors. To validate the model’s assumptions, data from the PZL-3W engine were used. The model was created by meticulously incorporating the engine’s specific design characteristics, allowing for an in-depth examination of how performance characteristics shift with adjustments to high-pressure rotor settings or changes in ambient conditions. The resulting calculations demonstrated a high level of agreement between the model’s output and empirical data available for the PZL-3W engine, as well as with data found in the relevant scientific literature. This alignment underscores the model’s robustness and reliability in simulating engine performance across a range of operating scenarios, making it a valuable tool for further engineering analyses and optimization of free power turbine engines and their possible application.

Miniaturized laser interferometer using dynamic current-wavelength modulation for high precision displacement measurement

Abstract This study proposes a compact displacement measurement interferometer facilitated by a novel dynamic current-wavelength modulation method for high-precision displacement measurement. This technique compensates for the modulation depth by precisely predicting the optical path difference with frequency-scanning interferometry to achieve consistent modulation depth over an extensive measurement range. Additionally, a wavelength-locking method was developed for the 1550 nm band, using the P7 absorption peak of hydrogen cyanide gas to lock the central wavelength of the laser. The experimental results showed that the wavelength stability can be kept within 128 fm for 8 h with the proposed method. Furthermore, the deviations are less than 40 nm compared to a calibrated laser interferometer within the 300 mm measurement range. This research offers precise positioning feedback for precision engineering, paving the way for advanced semiconductor manufacturing processes.

High Efficiency (>10%) AgBiS2 Colloidal Nanocrystal Solar Cells with Diketopyrrolopyrrole-Based Polymer Hole Transport Layer.

Silver bismuth disulfide (AgBiS2) colloidal nanocrystals (CNCs) have emerged as ecofriendly photoactive materials with excellent photoconductivity and high absorption coefficients, in compliance with the restriction of hazardous substances (RoHS) guidelines. To maximize the theoretical potential of AgBiS2 CNC solar cells, a new diketopyrrolopyrrole (DPP)-based polymer, BD2FCT, optimized as a hole transport layer (HTL), is developed. This asymmetric thiophene-rich polymer HTL effectively complements the optical absorption spectrum of CNCs and forms a homogeneous layer atop the CNCs, facilitating favorable vertical charge transfer through intrinsic molecular packing. Furthermore, the BD2FCT HTL aligns energetically with AgBiS2, significantly reducing charge recombination at the CNC/HTL interfaces and enhancing charge extraction and photocurrent generation across the entire optical absorption spectrum. These characteristics are further optimized through precise molecular engineering. Additionally, a low-bandgap acceptor, IEICO-4F, is structurally incorporated with the BD2FCT polymer to further improve charge funneling and complementary absorption. Transient absorption spectroscopy reveals enhanced hole transfer from CNC to BD2FCT-29DPP:IEICO-4F, resulting in reduced charge recombination and efficient charge extraction. Consequently, a BD2FCT-based AgBiS2 CNC solar cell achieves a power conversion efficiency (PCE) of 10.1%, demonstrating significant improvements in short-circuit current density (JSC) and fill factor (FF).

Strategies and Protocols for Optimized Genome Editing in Potato.

The potato family includes a highly diverse cultivar repertoire and has a high potential for nutritional yield improvement and refinement but must in line with other crops be adapted to biotic and abiotic stresses, for example, accelerated by climate change and environmental demands. The combination of pluripotency, high ploidy, and relative ease of protoplast isolation, transformation, and regeneration together with clonal propagation through tubers makes potato highly suitable for precise genetic engineering. Most potato varieties are tetraploid having a very high prevalence of length polymorphisms and small nucleotide polymorphisms between alleles, often complicating CRISPR-Cas editing designs and strategies. CRISPR-Cas editing in potato can be divided into (i) characterization of target area and in silico-aided editing design, (ii) isolation and editing of protoplast cells, and (iii) the subsequent explant regeneration from single protoplast cells. Implementation of efficient CRISPR-Cas editing relies on efficient editing at the protoplast (cell pool) level and on robust high-throughput editing scoring methods at the cell pool and explant level. Gene and chromatin structure are additional features to optionally consider. Strategies and solutions for addressing key steps in genome editing of potato, including light conditions and schemes for reduced exposure to hormones during explant regeneration, which is often linked to somaclonal variation, are highlighted.

MOF-derived phase-selective synthesis of ln2O3 with appropriate surface atomic arrangement for CO2 photoreduction

Crystal phase engineering emerges as a powerful strategy to enhance photocatalytic activity, yet controlled phase-selective synthesis and phase-dependent performance understanding remain challenging. In this study, we introduce a novel method for synthesizing phase-engineered In2O3 via the pyrolysis of metal–organic frameworks (MOFs). We demonstrate that the functional groups and pyrolysis temperatures of MOFs are critical for phase-selective synthesis. Specifically, pyrolysis of MIL-68(ln)–NH2 at optimal temperatures yields In2O3 with rhombohedral (rh-In2O3), cubic (c-In2O3), and rhombohedral/cubic heterophase (rh/c-In2O3) structures. Our photocatalytic CO2 reduction tests reveal that c-In2O3 outperforms rh-In2O3 and rh/c-In2O3, achieving a CO production rate of 29.19 μmol g-1h−1 with 94.47 % selectivity. Spectroscopic and theoretical analyses show that c-In2O3 has superior charge transfer efficiency and lower reaction energy barriers, particularly for the rate-determining *CO intermediate, which exhibits a lower Gibbs free energy on its surface. This work provides a significant advancement in optimizing photocatalytic CO2 reduction efficiency through precise phase engineering, underscoring the vital role of phase control in enhancing catalytic performance.

Mathematical modeling and simulations using software like MATLAB, COMSOL and Python

This study explores the application of MATLAB, COMSOL, and Python in mathematical modeling and simulation within precision engineering. These tools are analyzed for their strengths in handling various engineering challenges, from control systems to multiphysics simulations and custom algorithm development. The study also investigates the role of artificial intelligence (AI), in supporting mathematical modeling tasks by automating coding, providing concept explanations, and aiding model structuring. By comparing computational performance, accuracy, and usability, the research aims to identify the best-suited software for different simulation types, such as thermal-fluid dynamics and structural analysis. The findings underscore the significance of choosing appropriate software for optimizing computational resources, validating models, and achieving reliable, efficient simulations. This study contributes practical guidelines for bridging the gap between theoretical models and practical applications, enhancing productivity, and fostering innovation in precision engineering.

Silicon‐Nitride‐Integrated Hybrid Optical Fibers: A New Platform for Functional Photonics

AbstractHybrid optical fibers that integrate exotic materials within more traditional silica glass architectures open a route for the development of highly functional all‐fiber photonic systems. Here, a compact hybrid optical fiber platform is reported formed by depositing a silicon nitride (SiNx ‐ nitride‐rich) nanolayer onto the surface of fused‐silica microfibers via plasma‐enhanced chemical vapor deposition. The SiNx thickness can be precisely tuned over a range of tens of nanometers, while maintaining an ultra‐smooth deposition surface, allowing for tunable coupling between the modes guided predominantly in the nanolayer and the fiber core. The effective indices of the hybrid modes display an anti‐crossing behavior under resonant conditions, resulting in a rich dispersion landscape that can be tailored via adjusting the SiNx thickness. By fabricating a SiNx‐silica hybrid microfiber with precise dispersion engineering and a low insertion loss, a flat supercontinuum spectrum spanning >1.5 octaves (−20 dB level) has been generated. The results demonstrate that SiNx‐silica hybrid microfibers can offer a unique combination of broadband transmission and wide tunablity of the mode properties, while still retaining the benefits of robust integration with conventional silica glass fiber networks, providing a rich playground for hybrid fiber‐based photonic systems.

Precise Molecular Engineering of Multi‐Suborganelle Targeted NIR Type‑I AIE Photosensitizer and Design of Cell Membrane‐Anchored Anti‐Tumor Pyroptosis Vaccine

Precise Molecular Engineering of Multi‐Suborganelle Targeted NIR Type‑I AIE Photosensitizer and Design of Cell Membrane‐Anchored Anti‐Tumor Pyroptosis Vaccine

Overview of 3D Printing Multimodal Flexible Sensors.

With the growing demand for flexible sensing systems and precision engineering, there is an increasing need for sensors that can accurately measure and analyze multimode signals. 3D printing technology has emerged as a crucial tool in the development of multimodal flexible sensors due to its advantages in design flexibility and manufacturing complex structures. This paper provides a review of recent advancements in 3D printing technology within the field of multimode flexible sensors, with particular emphasis on the relevant working mechanisms involved in decoupling complex signals. First, the research status of 3D printed multimodal flexible sensors is discussed, including their responsiveness to different modal stimuli such as mechanics, temperature, and gas. Furthermore, it explores methods for decoupling multimodal signals through structural and material design, artificial intelligence, and other technologies. Finally, this paper summarizes current challenges such as limited material selection, difficulties in miniaturization integration, and crosstalk between multisignal outputs. It also looks forward to future research directions in this area.

A Polarization‐Insensitive and Adaptively‐Blazed Meta‐Grating Based on Dispersive Metasurfaces

AbstractThe diffraction efficiency of blaze gratings is optimized only at a specific frequency due to a fixed blaze angle, resulting in reduced and variable diffraction efficiencies over the working frequency band. Additionally, blazed gratings demonstrate polarization dependence due to their groove structures and the interaction of light with their surfaces. Consequently, designing gratings with constant diffraction efficiencies across a wide frequency bandwidth while maintaining polarization independence remains a challenge. Here, a design paradigm of dispersion engineerable meta‐grating inspired by orthogonal harmonic oscillations (OHO) is presented. Utilizing the OHO model, the phase dispersion of a metasurface can be precisely controlled, which applies to any unit cell featuring two orthogonal electromagnetic resonances. As a proof of concept, a polarization‐insensitive meta‐grating is showcased, where the blazed angle adapts with the incident frequency, ensuring broadband performance. In the experiment, the adaptively‐blazed grating measured an optimized and constant diffraction efficiency of ≈80% over the working wavelength range, i.e., 8.7–12.2 µm. The difference in diffraction efficiency between the two perpendicular linear polarization states remains within 4.6%. The proposed paradigm paves the way for meta‐device design based on precise dispersion engineering, which has potential applications in spectrometers, broadband beam forming and steering, hyperspectral imaging, etc.

Application of deep learning techniques for breath-hold, high-precision T2-weighted magnetic resonance imaging of the abdomen.

To evaluate the feasibility of a high-precision single-shot fast spin-echo (SS-FSE) sequence using the deep learning-based Precise IQ Engine (PIQE) algorithm in comparison with standard SS-FSE for T2-weighted MR imaging of the abdomen, and to compare the image quality with a multi-shot (MS)-FSE sequence using the PIQE algorithm. This retrospective study included 105 patients who underwent abdominal MR including T2-weighted sequences using the PIQE reconstruction algorithm. The image quality, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) in high-precision SS-FSE sequences using PIQE were compared to those in standard SS-FSE without PIQE and MS-FSE sequences using PIQE. The scores for all qualitative parameters were significantly higher in high-precision SS-FSE sequence using PIQE than in standard SS-FSE sequence without PIQE (all p < 0.001). In the comparison between two high-precision sequences using PIQE, the SS-FSE sequence showed significantly better scores for the blurring, ghosts or motion/flow artifacts, conspicuity of intrahepatic structures, focal nonsolid hepatic and pancreatic cystic lesions, and overall image quality, in comparison to the MS-FSE sequence (all p < 0.001). Additionally, the SS-FSE sequence using PIQE showed significantly higher SNR of the liver and CNR of nonsolid hepatic lesions than the MS-FSE sequence using PIQE (p < 0.001). A high-precision SS-FSE sequence using the PIQE algorithm is a feasible alternative to the standard FSE sequence in T2-weighted MR imaging of the abdomen. It can improve image quality, the SNR of the liver, and the ability to visualize nonsolid focal liver lesions and pancreatic cystic lesions in comparison to a high-precision MS-FSE sequence using PIQE although this study was limited by single-center design and lack of pathological confirmation.

High-precision MRI of liver and hepatic lesions on gadoxetic acid-enhanced hepatobiliary phase using a deep learning technique.

The purpose of this study was to investigate whether the high-precision magnetic resonance (MR) sequence using modified Fast 3D mode wheel and Precise IQ Engine (PIQE), that was collected in a wheel shape with sequential data filling in the k-space in the phase encode-slice encode plane, is feasible for breath-hold (BH) three-dimensional (3D) T1-weighted imaging of the hepatobiliary phase (HBP) of gadoxetic acid-enhanced MRI in comparison to the compressed sensing (CS) sequence using Advanced Intelligent Clear-IQ Engine (AiCE). This retrospective study included 54 patients with focal hepatic lesions who underwent dynamic contrast-enhanced MRI. Both standard HBP images using CS with AiCE and high-precision HBP images using modified Fast 3D mode wheel and PIQE were obtained. Image quality, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were evaluated using the Wilcoxon signed-rank test. p values of < 0.05 were considered to be statistically significant. Scores for image noise, conspicuity of liver contours and intrahepatic structures, and overall image quality in high-precision HBP imaging using modified Fast 3D mode wheel and PIQE were significantly higher than those in HBP imaging using CS and AiCE (all p < 0.001). There was no significant difference in the presence of artifact and motion-related blurring. There were no significant differences between the sequences in SNR (p = 0.341) or CNR (p = 0.077). The detection rate of focal hepatic lesions was 71.4-85.3% in CS with AiCE, and 82.2-95.8% in modified Fast 3D mode wheel and PIQE. A high-precision MR sequence using a modified Fast 3D mode wheel and PIQE is applicable for the HBP of BH 3D T1-weighted imaging.

Programmable orientation of blue phase soft photonic crystal

Understanding the structure formation and its underlying physical mechanism is a fundamental topic in condensed matter systems, with both academic and practical implications. Soft matter is playing a remarkable role in current era of information explosion, demonstrating enormous potential in integrated functional photonics. As unique soft photonic crystals with cubic symmetries, not only liquid crystalline blue phases (BPs) have circularly polarized selective reflection and ultra-fast electro-optical response, but also their three-dimensional photonic structures increase degrees-of-freedom for multiplexed optical modulation. In the thriving field of soft-matter-based photonics, precise and programmable engineering of BP crystal orientation is of vital importance for planar optical elements, which remains a challenging task due to the complexity of the nucleation process as well as the interaction between the BP building blocks and the boundary conditions. Aiming to gain a comprehensive understanding of how to tailor the orientation of BP crystals for the photonic applications of next generation, here we discuss the solutions for uniformity improvement and orientation control of BP crystals, about which a few of examples in combination with the underlying mechanisms are explained. In addition, the remaining challenges and the efforts that are expected are also reviewed. We expect this work provides a deeper understanding of phase transitions and resulting structures in soft crystals, which may open encouraging perspectives for their applications in photonics, biosensing, interfacial, and chemical engineering.

Photothermally Active Quantum Dots in Cancer Imaging and Therapeutics: Nanotheranostics Perspective.

Cancer is becoming a global threat, as the cancerous cells manipulate themselves frequently, resulting in mutants and more abnormalities. Early-stage and real-time detection of cancer biomarkers can provide insight into designing cost-effective diagnostic and therapeutic modalities. Nanoparticle and quantum dot (QD)-based approaches have been recognized as clinically relevant methods to detect disease biomarkers at the molecular level. Over decades, as an emergent noninvasive approach, photothermal therapy has evolved to eradicate cancer. Moreover, various structures, viz., nanoparticles, clusters, quantum dots, etc., have been tested as bioimaging and photothermal agents to identify tumor cells selectively. Among them, QDs have been recognized as versatile probes. They have attracted enormous attention for imaging and therapeutic applications due to their unique colloidal stability, optical and physicochemical properties, biocompatibility, easy surface conjugation, scalable production, etc. However, a few critical concerns of QDs, viz., precise engineering for molecular imaging and sensing, selective interaction with the biological system, and their associated toxicity, restrict their potential intervention in curing cancer and are yet to be explored. According to the U.S. Food and Drug Administration (FDA), there is no specific regulation for the approval of nanomedicines. Therefore, these nanomedicines undergo the traditional drug, biological, and device approval process. However, the market survey of QDs is increasing, and their prospects in translational nanomedicine are very promising. From this perspective, we discuss the importance of QDs for imaging, sensing, and therapeutic usage pertinent to cancer, especially in its early stages. Moreover, we also discuss the rapidly growing translational view of QDs. The long-term safety studies and cellular interaction of these QDs could enhance their visibility and bring photothermally active QDs to the clinical stage and concurrently to FDA approval.

Engineering Ion Affinity of Zr-MOF Hybrid PDMS Membranes for the Selective Separation of Na+/Ca2.

Ion-selective separation, especially Na+/Ca2+ separation, is of significant importance in the realms of biomimetic research and the fabrication of biomimetic devices, underscoring the pivotal role that sodium and calcium ions play in cellular metabolism. However, the analogous ionic radii and charge densities shared by sodium and calcium ions significantly impede their effective discrimination, presenting formidable challenges for the precise engineering of ion separation materials, such as separation membranes. In this study, a polydimethylsiloxane (PDMS) separation membrane hybridized with zirconium-based metal-organic frameworks (UiO-66, UiO-66-NO2 and UiO-66-NH2) was constructed. Through the meticulous design of the MOF functional groups, the material's affinity for specific ions was modulated, thereby achieving efficient Na+/Ca2+ separation. Notably, the PDMS integrated with amino-modified Zr-MOF exhibited an efficacious selective separation of Na+ and Ca2+ ions. The interaction between the amino group of UiO-66-NH2 and Ca2+ gave rise to the observed superior selectivity toward Ca2+ cations and enhanced separation efficiencies of up to 64% compared to pristine PDMS for UiO-66-NH2-embedded membranes.

Harnessing the optimization of enzyme catalytic rates in engineering of metabolic phenotypes.

The increasing availability of enzyme turnover number measurements from experiments and of turnover number predictions from deep learning models prompts the use of these enzyme parameters in precise metabolic engineering. Yet, there is no computational approach that allows the prediction of metabolic engineering strategies that rely on the modification of turnover numbers. It is also unclear if modifications of turnover numbers without alterations in the host's transcriptional regulatory machinery suffice to increase the production of chemicals of interest. Here, we present a constraint-based modeling approach, termed Overcoming Kinetic rate Obstacles (OKO), that uses enzyme-constrained metabolic models to predict in silico strategies to increase the production of a given chemical, while ensuring specified cell growth. We demonstrate that the application of OKO to enzyme-constrained metabolic models of Escherichia coli and Saccharomyces cerevisiae results in strategies that can at least double the production of over 40 compounds with little penalty to growth. Interestingly, we show that the overproduction of compounds of interest does not entail only an increase in the values of turnover numbers. Lastly, we demonstrate that a refinement of OKO, allowing also for manipulation of enzyme abundance, facilitates the usage of the available compendia and deep learning models of turnover numbers in the design of precise metabolic engineering strategies. Our results expand the usage of genome-scale metabolic models toward the identification of targets for protein engineering, allowing their direct usage in the generation of innovative metabolic engineering designs for various biotechnological applications.

Bioinspired Surface Engineering with Dual Covalent Receptors Incorporated via Precise Post-Imprinting Modification to Enhance the Specific Identification of Adenosine 5′-Monophosphate

Expanding the specific surface area of substrates and carrying out precise surface engineering of imprinted nanocavities are crucial methods for enhancing the identification efficiency of molecularly imprinted polymers (MIPs). To implement this synergistic strategy, bioinspired surface engineering was used to incorporate dual covalent receptors via precise post-imprinting modifications (PIMs) onto mesoporous silica nanosheets. The prepared sorbents (denoted as “D-PMIPs”) were utilized to improve the specific identification of adenosine 5′-monophosphate (AMP). Significantly, the mesoporous silica nanosheets possess a high surface area of approximately 498.73 m2∙g–1, which facilitates the formation of abundant specific recognition sites in the D-PMIPs. The dual covalent receptors are valuable for establishing the spatial orientation and arrangement of AMP through multiple cooperative interactions. PIMs enable precise site-specific functionalization within the imprinted cavities, leading to the tailor-made formation of complementary binding sites. The maximum number of high-affinity binding sites (Nmax) of the D-PMIPs is 39.99 μmol∙g–1, which is significantly higher than that of imprinted sorbents with a single receptor (i.e., S-BMIPs or S-PMIPs). The kinetic data of the D-PMIPs can be effectively described by a pseudo-second-order model, indicating that the main binding mechanism involves synergistic chemisorption from boronate affinity and the pyrimidine base. This study suggests that using dual covalent receptors and PIMs is a reliable approach for creating imprinted sorbents with high selectivity, allowing for the controlled engineering of imprinted sites.