Objective: This narrative review aimed to evaluate scientific evidence on nanostructured biomaterials designed to improve implant osseointegration and reduce failure rates in medical and dental applications. Methodology: A comprehensive search was conducted in PubMed, Web of Science, and Google Scholar using the terms “nanotopography”, “smart biomaterials”, “osseointegration”, and “implant surface modification”. Duplicates were removed, and studies were screened by title, abstract, and full-text analysis. Eligible studies included preclinical or clinical evaluations of nanostructured surfaces and their biological performance. Results: Evidence demonstrated that controlled nanotopographic surfaces enhance osteoblast differentiation, bone anchorage, and healing speed. Biomaterials with specific nanoscale patterns showed superior biological performance and reduced inflammatory response compared to conventional implants. Conclusion: Smart surface engineering based on nanostructure modulation significantly improves implant integration. These materials mark a transition from passive mechanical fixation to biologically interactive systems, advancing regenerative medicine and implantology.

As inspired by natural cilia, smart (stimuli-responsive) artificial microwires can manipulate objects and tune surface properties, leading to various applications. However, artificial microwires operate merely in gentle working conditions due to their inherent fragility against mechanical damage, indicating that there is a critical gap between the microwires demonstrated in a laboratory and the microwires that operate in practical conditions. This inherent limitation is circumvented by introducing a new surface design, where the microwires are hidden in interconnected frames when facing mechanical damage and can be aroused by external magnetic fields to deliver functionalities when needed. Namely, under a magnetic field parallel to a substrate textured with vertical frames, iron-laden polydimethylsiloxane (PDMS) aerosols are aligned perpendicularly onto frame sidewalls, forming multilayer microwires parallel to the substrate. The frames prevent mechanical damage from contacting the wires, and the lower layers of wires are functional, although the top layers are worn off, rendering the mechanical robustness. By applying a magnetic field, the wires can re-align uprightly and hence expose themselves to the working environment, delivering functionalities such as on-demand control in droplet impact dynamics, adhesion force, and transport. This design strategy paves the way for the utilization of smart surfaces in real-life conditions.

The main contribution of this manuscript is an innovative framework for integrating Artificial Intelligence (AI) in 6G wireless systems. With increased complexity, including bursty traffic, network complexity, and dynamic variability, there is a need for intelligence. This study develops and validates an AI-driven approach that enhances network performance through quantum communication decoding, beamforming, and decentralized edge processing. Kalman filtering predictive models are used to estimate variable channel conditions in a Terahertz (THz) network to support beamforming to optimize beamforming. Artificial Intelligence exploits smart reflective surfaces (IRS) strengthening signals and improving their coverage. Also, strong security of Quantum Key Distribution (QKD) protocols due to AI enhanced error correction technology, and rapid, yet privacy information conducting at edge nodes due to decentralised processing through federated learning are examples of enhanced capabilities. Extensive ns-3 simulations across 100 independent runs validate the framework’s effectiveness and prove the system in practical 6G deployment scenarios including THz links, IRS component and edge nodes. The simulation results demonstrate that the proposed framework achieves superior performance compared to conventional approaches, with statistical validation across multiple deployment scenarios. The system decreases latency by 30%, and adds 25% to spectral efficiency. In bursty traffic, the energy efficiency is increased by 20% and packets delivery ratio (PDR) is boosted by 15%. The AI algorithms work effectively to regulate the channel estimation, beamforming, and resource allocation, and, as a result, showed an improvement in the order of magnitudes over previous studies. These results support the fact that AI demonstrates significant potential for transformative impact to a 6G network. The framework has been efficient in addressing problems of channel estimation, beamforming and distributed processing and novel calculations in quantum communication security protocols. Such findings can be used as the foundation of the further inclusion of AI-based technologies in 6G systems, which will help to deploy robust, resilient, and autonomous wireless networks to address the needs of a connective society.

The development of efficient and stable photocathodes is critical for advancing photoelectrochemical (PEC) CO₂ reduction technologies toward practical applications. In this presentation, we present a series of engineered copper oxide-based heterostructures designed to enhance charge separation, suppress photocorrosion, and improve product selectivity toward valuable C₂⁺ compounds.A key advancement is the Cu₂O/Zn₅(OH)₈Cl₂ (CZHC) heterostructure, which combines improved charge carrier dynamics and surface stability with enhanced CO₂ adsorption capacity. At 0.4 V vs. RHE, this architecture demonstrates a stable photocurrent and high faradaic efficiency for methanol, ethanol, propylene, and butadiene formation. Density functional theory (DFT) supports these results, showing stronger CO₂ binding and more efficient electron transfer at the interface.Complementary approaches using hierarchical CuFe₂O₄ core–shells and CuNiOₓ composites further extend the stability and selectivity of Cu-based photocathodes. These systems suppress competing hydrogen evolution and promote C₂⁺ product pathways, as confirmed by EIS and product analysis.Together, these results offer a robust strategy for designing next-generation PEC CO₂ reduction systems with high efficiency and long-term durability.

This research introduces a model for defect detection in smart surface manufacturing processes employing a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN). Use the suggested strategy, which looks at both time and place, to find problems with production systems as they happen. Convolutional neural networks (CNNs) can help you uncover exact spatial patterns, while recurrent neural networks (RNNs) can help you find the order of mistakes over time. Lab tests reveal that the technology is better than older versions in terms of accuracy, precision, and reliability. This proves that it can be utilised in smart manufacturing. The model can work in real time because of latency and performance metrics. This study introduces a versatile and customisable quality control system to meet the growing requirements of Industry 4.0 and the imminent progress in intelligent manufacturing.

Hyaluronidase-responsive polymeric nanomaterials for targeted antimicrobial therapy: Structure-function relationships and biomaterial design strategy.

Real-time driving monitoring systems can use self-powered sensors based on artificial intelligence (AI) and triboelectric nanogenerators (TENGs). Here, we created a TENG-based self-powered intelligent steering wheel that can detect hand gripping. The TENG serves as the steering wheel’s smart surface. In addition to monitoring the steering wheel in real time, the intelligent steering wheel reacts quickly. The TENG sensor can detect hazardous conditions and lower processing demands while retaining excellent identification accuracy when used in conjunction with machine learning. Additionally, the TENG sensor may now offer an accurate and affordable monitoring solution for smart driving thanks to the integration of AI.

An optimization based framework for water quality assessment and pollution source apportionment employing GIS and machine learning techniques for smart surface water governance

COLoRIS: Localization-Agnostic Smart Surfaces Enabling Opportunistic ISAC in 6G Networks

All glass represents a material with extremely high utility potential in the development of biomaterials and research tools. Due to a number of its unique properties, such as chemical inertness, thermal stability, and transparency, it can be used in the preparation of hybrid materials for medicine and biotechnology. Such materials can be obtained by grafting polymer brushes from glass surface by reversible deactivation radical polymerization (RDRP) techniques. This paper provides a literature review of the foregoing advances in the development of glass surface modification concepts using atom transfer radical polymerization (ATRP) and reversible addition-fragmentation chain transfer polymerization (RAFT). These methods are particularly attractive in designing smart coatings because they enable the synthesis of polymers with a well-defined structure and low dispersity. The resulting materials can then serve as antimicrobial surfaces, tools for selective manipulation of cells, and intelligent platforms for creating cell sheets in tissue engineering. Therefore, the idea of glass modification using RDRP techniques appears to be a promising concept for the future in the development of smart materials for various applications.

The combination of drones and smart reconfigurable surfaces (RISs) is becoming increasingly important for improving energy efficiency and wireless communication performance in Internet of Things (IoT) networks. This research focuses on developing an iterative optimization algorithm based on the fmincon algorithm in MATLAB. Sensing and transmission parameters are updated simultaneously at each iteration to achieve maximum energy efficiency. The algorithm starts with initial values. To optimize the energy efficiency of a system integrating a drone that provides mobile edge computing (MEC) services to IoT devices, the proposed system takes into account several critical factors, including drone trajectory optimization, optimal bit allocation between local and drone processing, and phase shift optimization in smart reconfigurable surfaces. The goal is to maximize overall energy efficiency by jointly optimizing these elements through a novel algorithm that alternates between optimizing the smart reconfigurable surfaces' phase shifts, the drone trajectory, and bit allocation. Simulation results demonstrate that the proposed solution significantly outperforms other measurement approaches in terms of energy efficiency, while examining the impact of variables such as the number of users, the reflectance elements of reconfigurable smart surfaces, and base station antennas on system performance. In conclusion, this research presents a novel approach to enhancing energy efficiency in RIS-enabled and drone-enabled MEC systems for IoT networks, achieving significant improvements over existing methods.

Abstract This paper presents the design, implementation, and characterization of a compact two-stage analog phase-shifter for the Ka-band, based on thin-film technology. The design utilizes a reflective-type configuration, employing four metal-insulator-graphene diodes as reflective loads. The fabricated prototype is realized on an 8-µ m-thick flexible polyimide substrate and occupies less than 0.7 mm2 of chip area including the contact pads. Performance evaluation of the fabricated circuit reveals an S11 of better than −13 dB and an S21 of −3.3 dB with a tolerance of ± 0.5 dB across the frequency band from 28 to 36 GHz, along with a tunable phase difference ( $\Delta\phi$ ) exceeding 70∘. The introduced flexible thin-film technology promotes the realization of flexible cost-effective beam steering for smart surfaces implementations for communication and biomedical applications.

Slippery surfaces with covalently attached liquid-like polymer brushes have gained increasing research attention as unique liquid-repellent surfaces with dynamic omniphobic properties and excellent biofouling resistance. However, ultrafine patterning of such surfaces has yet to be explored in a straightforward manner. This work reports a facile polydimethylsiloxane (PDMS) stamp-based transfer printing approach to generate highly uniform patterns with submicrometer sizes on slippery liquid-like surfaces over large areas. The microprism-shaped PDMS stamp soaked with concentrated ammonia solution is pressed onto a slippery substrate modified with linear PDMS brushes, leaving uniform line arrays with widths of ∼500 nm and heights of ∼50-100 nm. The patterning process is investigated in detail to elucidate the properties of the patterned structures and the effect of patterning conditions including the alkali treatment, contact time, pressure, environmental temperature, etc. The volatile ammonia is verified as essential to promote the transfer of silica-like/silicone residues from the stamp, leading to discontinuous nanodot or continuous nanoline features, depending on the amount of transferred silicone to allow the capillary assembly. Furthermore, the patterned PDMS brush surface provides a versatile platform for facile production of biomicroarrays, in which the generated pattern arrays are found to immobilize proteins selectively while the background contamination is minimized by the liquid-like antifouling coating. These results illustrate the application potential of the well-defined patterned slippery surfaces in a wide variety of fields including smart surfaces, biochips, and biosensors, as well as micro-/nanoscale optoelectronic devices.

Abstract Dynamic shape‐morphing soft surfaces are widespread in biological systems and hold great promise for a variety of applications. Despite considerable efforts, challenges remain in achieving fast, repetitive, precise, and contactless control over the desired surface morphing. Here, this work presents a bio‐inspired approach that leverages magnetically actuated dome snapping for fast and reprogrammable hydrogel surface morphing. The system consists of a sheet incorporating an array of swelled magnetic gels dispersed within nonswelling regions, forming bistable domes upon swelling. When a magnet approaches the side opposite to the bulking direction of a magnetic gel dome, the dome snaps rapidly toward the magnetic field due to direct magnetic interactions. By tailoring the magnetic threshold for the magnetic dome snapping, adjusting the spatial distribution of magnetic domes within the hydrogel, and precisely controlling the magnetic field, the hydrogel surface can dynamically morph in a programmable, ultrafast, and contactless manner. This work utilizes magnetically actuated surface morphing for dynamic displays, information encryption and decryption, and selective object manipulation. This work is expected to advance magnetically controlled soft robotics with multifunctional smart surfaces, unlocking a wide range of application possibilities.

Abstract In this study, the thermomechanical buckling behavior of sandwich smart nanoplates with magneto-electro-elastic surface layers was modeled and examined together with high-order plate theory and nonlocal strain gradient elasticity theory. It consists of a functionally graded material (FGM) metal-ceramic foam structure containing four different foam distributions in the core layer of the sandwich nanoplate. FGM core structure includes pure metal, pure ceramic, pure ceramic–metal and pure metal-cerasssmic combinations. The equations of motion were obtained by Hamilton’s principle as a result of the electro-elastic and magneto-strictive coupling effects, as well as the reflection of thermal loads, spring foundation and shear foundation effects into the energy equations, and the equations of motion were solved by the Navier method. Thermal effects, foundation effects, the effects of electric and magnetic potentials applied to the smart surface layers, and the effects of the properties of the foam structure in the core layer on the thermo-mechanical buckling behavior of the smart sandwich nanoplate have been examined in a broad framework. It is thought that the results of this study will be beneficial in the design and production of smart nano electro-mechanical systems that are intended to operate in high temperature environments. The buckling behavior of the smart plate can be adjusted with the properties of the core layer, the properties of the foundation coefficients and the applied external electric and magnetic potentials for a desired temperature operating environment.

Visible-light-triggered recovery of biologically intact cells using smart fluoropolymer-nanocoated materials.

Haptic interface is essential for improved user interaction with various displays by the sense of touch. However, current haptics in most displays provide limited tactile feedbacks due to their narrow operating frequencies. We present a multifunctional haptic display with a large‐area single crystal actuator which allows more diverse user experiences with a broad range of frequencies. The haptic actuator composed of the single crystal leads to high vibrational performance originated from its superior electromechanical properties. Furthermore, the automotive “Smart Surface” organic light‐emitting diode (OLED) display integrated with the actuator suggests a new direction for future interactive display applications.

This study aims to improve the performance of reconfigurable STAR-passive-OIRS in multiple input multiple output-based free-space optical (FSO) communication systems. The study focuses on analysing the influence of two main factors: the number of reflective elements and the location of the smart surface. STAR-RIS technology has shown great potential in improving spectrum efficiency and signal-to-noise and interference ratio (SINR) and in increasing channel gain by providing 360° coverage and reducing interference in environments with line-of-sight constraints. Results reveal that increasing the number of reflective elements enhances signal strength and improves SINR, whilst placing the smart surface closer to the source or receiver achieves optimal performance. The study highlights STAR-RIS technology’s ability to overcome traditional FSO systems’ limitations and improve network performance in modern wireless communication environments.

Abstract Despite significant advances in cancer treatment, several challenges persist in optimizing effective cargo delivery, including enhancing bioavailability, improving targeted delivery, and overcoming biological barriers for improved tumor tissue penetration. There is an urgent need for versatile carriers capable of multi‐functional targeting without compromising functionality. Here, we report a dual surface modification strategy to enhance the therapeutic efficacy of microrobotic platforms, through controlled, site‐specific drug release. This dual functionalization integrates two distinct pH‐sensitive polymeric nanoreservoirs with different membrane permeability. One nanoreservoir is engineered to release an antitumor agent ‐curcumin‐ in response to the acidic tumor microenvironment, while the second is designed to degrade the tumor extracellular matrix via enzymatic activity, facilitating enhanced diffussion of the therapeutic agent. This dual surface modification approach represents a significant advancement in the customizable integration of multifunctional nanoreservoirs. By leveraging dual compartmentalization, it prevents deactivation and cross‐process interference, enabling precise nanoscale combination therapies for microrobotic cancer treatment. These surface‐engineered microrobots hold promise for overcoming physiological barriers, ensuring stable cargo transport, and broadening the applicability of microrobotic platforms across diverse cancer types.

Light-Activated Nanobumps: Photoresponsive Low-Density Azobenzene Self-Assembled Monolayers for Smart Surfaces