Selective Tuning Research Articles

Controllable Regulation of CO2 Adsorption Behavior via Precise Charge Donation Modulation for Highly Selective CO2 Electroreduction to Formic Acid.

The synthesis of value-added products via CO2 electroreduction (CO2ER) is of great significance, but the development of efficient and versatile strategies for the controllable selectivity tuning is extremely challenging. Herein, the tuning of CO2ER selectivity through the modulation of CO2 adsorption behavior is proposed. Using the constructed zeolitic MOF (SNNU-339), CO2 adsorption behavior is controllably changed from *CO2 to CO2* via the precise ligand-to-metal charge donation (LTMCD) regulation. It is confirmed that the high electronegativity of the coordinate ligand directly restricts the LTMCD, reduces the charge density on the metal sites, lowers the Gibbs free energy for CO2* adsorption, and leads to the transformation of CO2 adsorption mode from *CO2 to CO2*. Owing to the modulated CO2 adsorption behavior and regulated kinetics, SNNU-339 exhibits superior HCOOH selectivity (≈330% promotion, 85.6% Faradaic efficiency) and high CO2ER activity. The wide applicability of the proposed approach sheds light on the efficient CO2ER. This study provides a competitive strategy for rational catalyst design and underscores the significance of adsorption behavior tuning in electrocatalysis.

Feature Extraction and Identification of Rheumatoid Nodules Using Advanced Image Processing Techniques

Background/Objectives: Accurate detection and classification of nodules in medical images, particularly rheumatoid nodules, are critical due to the varying nature of these nodules, where their specific type is often unknown before analysis. This study addresses the challenges of multi-class prediction in nodule detection, with a specific focus on rheumatoid nodules, by employing a comprehensive approach to feature extraction and classification. We utilized a diverse dataset of nodules, including rheumatoid nodules sourced from the DermNet dataset and local rheumatologists. Method: This study integrates 62 features, combining traditional image characteristics with advanced graph-based features derived from a superpixel graph constructed through Delaunay triangulation. The key steps include image preprocessing with anisotropic diffusion and Retinex enhancement, superpixel segmentation using SLIC, and graph-based feature extraction. Texture analysis was performed using Gray-Level Co-occurrence Matrix (GLCM) metrics, while shape analysis was conducted with Fourier descriptors. Vascular pattern recognition, crucial for identifying rheumatoid nodules, was enhanced using the Frangi filter. A Hybrid CNN–Transformer model was employed for feature fusion, and feature selection and hyperparameter tuning were optimized using Gray Wolf Optimization (GWO) and Particle Swarm Optimization (PSO). Feature importance was assessed using SHAP values. Results: The proposed methodology achieved an accuracy of 85%, with a precision of 0.85, a recall of 0.89, and an F1 measure of 0.87, demonstrating the effectiveness of the approach in detecting and classifying rheumatoid nodules in both binary and multi-class classification scenarios. Conclusions: This study presents a robust tool for the detection and classification of nodules, particularly rheumatoid nodules, in medical imaging, offering significant potential for improving diagnostic accuracy and aiding in the early identification of rheumatoid conditions.

Optimizing Machine Learning-Based Intrusion Detection Systems for Enhanced Security in Cloud Computing Environments.

In today’s cloud computing environments, robust network security is crucial due to the inherently distributed and dynamic nature of cloud systems. Traditional Network Intrusion Detection Systems (NIDS) often face challenges in handling large-scale, rapidly evolving data and sophisticated attack vectors unique to cloud settings. This paper presents an optimized Machine Learning (ML)-based NIDS framework designed specifically for cloud infrastructures, leveraging feature selection, dimensionality reduction, and algorithmic tuning techniques to enhance detection accuracy and reduce false positives. Integrating supervised and unsupervised learning methods, this NIDS system can detect both known and novel attack types efficiently. By utilizing cloud-specific datasets and real-time traffic monitoring, it adapts well to the scalability and elasticity requirements of cloud environments. Additionally, the system incorporates automated optimization techniques, such as hyperparameter tuning and ensemble learning, to further enhance performance. Experimental results show improvements in detection rates, reduced computational overhead, and better scalability, making it a promising solution for modern cloud-based infrastructures.

Using fMRI to examine nonlinear mixed selectivity tuning to task and category in the human brain

Abstract Recent experimental and theoretical work has shown that nonlinear mixed selectivity, where neurons exhibit interaction effects in their tuning to multiple variables (e.g., stimulus and task) plays a key role in enabling the primate brain to form representations that can adapt to changing task contexts. Thus far all such studies have relied on invasive neural recording techniques. In this study, we demonstrate the feasibility of measuring nonlinear mixed selectivity tuning in the human brain noninvasively using fMRI pattern decoding. To do so, we examined the joint representation of object category and task information across human early, ventral stream, and dorsal stream areas while participants performed either an oddball detection task or a one-back repetition detection task on the same stimuli. These tasks were chosen to equate spatial, object-based, and feature-based attention, in order to test whether task modulations of visual representations still occur when the inputs to visual processing are kept constant between the two tasks, with only the subsequent cognitive operations varying. We found moderate but significant evidence for nonlinear mixed selectivity tuning to object category and task in fMRI response patterns in both human ventral and dorsal areas, suggesting that neurons exhibiting nonlinear mixed selectivity for category and task not only exist in these regions, but cluster at a scale visible to fMRI. Importantly, while such coding in ventral areas corresponds to a rotation or shift in the object representational geometry without changing the representational content (i.e., with the relative similarity among the categories preserved), nonlinear mixed selectivity coding in dorsal areas corresponds to a reshaping of representational geometry, indicative of a change in representational content.

A New Computer-Aided Diagnosis System for Breast Cancer Detection from Thermograms Using Metaheuristic Algorithms and Explainable AI

Advances in the early detection of breast cancer and treatment improvements have significantly increased survival rates. Traditional screening methods, including mammography, MRI, ultrasound, and biopsies, while effective, often come with high costs and risks. Recently, thermal imaging has gained attention due to its minimal risks compared to mammography, although it is not widely adopted as a primary detection tool since it depends on identifying skin temperature changes and lesions. The advent of machine learning (ML) and deep learning (DL) has enhanced the effectiveness of breast cancer detection and diagnosis using this technology. In this study, a novel interpretable computer aided diagnosis (CAD) system for breast cancer detection is proposed, leveraging Explainable Artificial Intelligence (XAI) throughout its various phases. To achieve these goals, we proposed a new multi-objective optimization approach named the Hybrid Particle Swarm Optimization algorithm (HPSO) and Hybrid Spider Monkey Optimization algorithm (HSMO). These algorithms simultaneously combined the continuous and binary representations of PSO and SMO to effectively manage trade-offs between accuracy, feature selection, and hyperparameter tuning. We evaluated several CAD models and investigated the impact of handcrafted methods such as Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), Gabor Filters, and Edge Detection. We further shed light on the effect of feature selection and optimization on feature attribution and model decision-making processes using the SHapley Additive exPlanations (SHAP) framework, with a particular emphasis on cancer classification using the DMR-IR dataset. The results of our experiments demonstrate in all trials that the performance of the model is improved. With HSMO, our models achieved an accuracy of 98.27% and F1-score of 98.15% while selecting only 25.78% of the HOG features. This approach not only boosts the performance of CAD models but also ensures comprehensive interpretability. This method emerges as a promising and transparent tool for early breast cancer diagnosis.

Foraging in the darkness: Highly selective tuning of below‐ground larval olfaction to Brassicaceae volatiles in striped flea beetle

AbstractThe olfactory system of above‐ground insects is among the best described perceptual architectures. However, remarkably little is known about how below‐ground insects navigate in the dark for foraging. Here, we investigated host plant preferences, olfactory sensilla and characterise olfactory proteins in below‐ground larvae of the striped flea beetle (SFB) Phyllotreta striolata Fabricius (Coleoptera: Chrysomelidae). Both the adults and larvae of this coleopteran pest cause serious damage to Brassicaceous crops above and below ground, respectively. To elucidate the role of olfactory system in host location of below‐ground larvae, we initially demonstrated that SFB larvae distinctly favoured Brassicaceae over other plant families by two‐choice behavioural bioassay. Subsequently, scanning electron microscopy of sensilla in SFB larval head showed a significant reduction in the number of olfactory sensilla in larvae compared with adults. However, essential olfactory sensilla such as sensilla basiconica are underscoring the indispensability of the larval olfactory system. We selected four larval‐specific odorant binding proteins for functional validation from our previous transcriptome data. Functional studies revealed that PstrOBP23 exhibits robust binding affinity to 24 volatiles of Brassicaceae plants, including seven isothiocyanate compounds. This suggests a pivotal role of PstrOBP23 in the foraging behaviour of the larvae below the ground. Moreover, two ligands displaying strong binding capacity exhibit apparent attractive or repellent activity towards SFB larvae. Our findings provide a crucial insight into the olfactory system of below‐ground larvae in SFB, highlighting the highly selective tuning of larvae specific OBP to host plant volatiles. These results offer potential avenues for developing effective pest control strategies against SFB.

Tunable Ag-Ox coordination for industrial-level carbon-negative CO2 electrolysis

The electrochemical CO2 reduction (eCO2RR) is promising for eliminating CO2, generate valuable chemicals and utilize spare electricity. However, its industrialization is greatly hindered by low CO2 single-pass conversion efficiency (SPCE) in alkaline/neutral electrolytes, and poor compatibility between the electrolysis and intermittent energy system. Herein, a carbon-negative acidic eCO2RR system is developed using acidic-tolerant Ag based metal-organic frameworks (Ag-MOFs), achieving a high Faradaic efficiency of CO above 97 % in a broad working window of 10–400 mA cm−2 for unsaturated Ag-O2 clusters, and a high CO2 SPCE of 46.3 % even under industrial-level 400 mA cm−2. Being connected to solar cells, the acidic eCO2RR system displays a remarkable solar-to-chemical efficiency of 19.74 %, offering great potential for industrial eCO2RR directly driven by renewable energy. Specifically, the CO/HCOOH selectivity could be manipulated by adjusting the coordination number of Ag atoms in Ag-MOFs, and thus a Pourbaix-diagram is proposed to explain the tunable selectivity theoretically.

Design and Optimization of Selectivity-Tunable Toll-like Receptor 7/8 Agonists as Novel Antibody-Drug Conjugate Payloads.

Toll-like receptors 7 and 8 are involved in modulating the adaptive and innate immune responses, and their activation has shown promise as a therapeutic strategy in the field of immuno-oncology. While systemic exposure to TLR7/8 agonists can result in poor tolerance, combination therapies and targeted delivery through antibody-drug conjugates (ADCs) can help mitigate adverse effects. Described herein is the identification of a novel and potent series of pyrazolopyrimidine-based TLR7/8 agonists with tunable receptor selectivity. Representative agonists from this series were successfully able to induce the production of various proinflammatory cytokines and chemokines from human peripheral blood mononuclear cells. Anti-HER2-25 and anti-HER2-26 ADCs made from this class of payloads demonstrated mechanism-based activation of TLR7/8 in a THP1/N87 coculture system.

Machine learning-based prediction model for the yield of nitrogen-enriched biomass pyrolysis products: Performance evaluation and interpretability analysis

The process optimization and control of nitrogen-rich biomass pyrolysis technology is crucial. This technology effectively converts agricultural waste into high-value energy products. The aim of this study is to develop an accurate model for predicting the yield of nitrogen-rich biomass pyrolysis products using machine learning techniques. The goal is to improve both the yield and quality of the products. This study uses machine learning techniques to develop an accurate model for predicting the yield of three-phase products from nitrogen-rich biomass pyrolysis. The goal is to optimize the biomass energy conversion process. The study builds a dataset containing 468 samples. This dataset comprehensively characterizes the chemical, elemental, and industrial properties of biomass, as well as the pyrolysis operating parameters. Models with high prediction accuracy are achieved by comparing various machine learning algorithms. These algorithms include Random Forest, GBDT, SVR, and ANN. Fine feature selection and hyper-parameter tuning are used to enhance the performance. In particular, the optimized GBDT model achieves a performance of MSE of 10.04 and R² of 0.92 in cross-validation. SHAP value analysis is used to further interpret the prediction results of the model. Sensitivity analysis, conducted through Monte Carlo simulation, identifies key features that have a high impact on the target value. Additionally, two-feature partial correlation analysis reveals the importance and dependence of the model features. This provides crucial technical support and a scientific basis for the industrial application of biomass energy and the optimization of the pyrolysis process.

Dynamically Tunable Circularly Polarized Selectivity in Plasmon-Enhanced Halide Perovskite Nanocrystal Glasses.

Ultrafast spin manipulation for optical spin-logic applications requires material systems with strong spin-selective light-matter interactions. The optical Stark effect can realize spin-selective light-matter interactions by breaking the degeneracy of spin-selective transitions with an external electric field. Halide perovskites have large exciton binding energies, which enable a room-temperature optical Stark effect. However, halide perovskites are prone to degradation when interacting with light and polar solvents, limiting further integration with nanophotonic structures. We demonstrate a hybrid material system consisting of CsPbBr3 nanocrystal glass weakly coupled to resonant plasmonic silver nanoparticles, showing ultrafast tunable spin-based polarization selectivity at room temperature. We performed circularly polarized pump-probe characterizations to investigate the optical Stark effect in this material system, which resulted in a maximum energy shift of ∼3.67 meV (detuning energy of 0.11 eV and pump intensity of 0.62 GW/cm2). We show that halide perovskite nanocrystal glasses have excellent resistance to heat and moisture, which may be favorable for integration with nanophotonic structures for further engineering polarization states, energy tuning, and coherence time.

Machine learning surveillance of foodborne infectious diseases using wastewater microbiome, crowdsourced, and environmental data

Clostridium perfringens (CP) is a common cause of foodborne infection, leading to significant human health risks and a high economic burden. Thus, effective CP disease surveillance is essential for preventive and therapeutic interventions; however, conventional practices often entail complex, resource-intensive, and costly procedures. This study introduced a data-driven machine learning (ML) modeling framework for CP-related disease surveillance. It leveraged an integrated dataset of municipal wastewater microbiome (e.g., CP abundance), crowdsourced (CP-related web search keywords), and environmental data. Various optimization strategies, including data integration, data normalization, model selection, and hyperparameter tuning, were implemented to improve the ML modeling performance, leading to enhanced predictions of CP cases over time. Explainable artificial intelligence methods identified CP abundance as the most reliable predictor of CP disease cases. Multi-omics subsequently revealed the presence of CP and its genotypes/toxinotypes in wastewater, validating the utility of microbiome-data-enabled ML surveillance for foodborne diseases. This ML-based framework thus exhibits significant potential for complementing and reinforcing existing disease surveillance systems.

Single-objective and multi-objective mixed-variable grey wolf optimizer for joint feature selection and classifier parameter tuning

Feature selection plays an essential role in data preprocessing, which can extract valuable information from extensive data, thereby enhancing the performance of machine learning classification. However, existing feature selection methods primarily focus on selecting feature subsets without considering the impact of classifier parameters on the optimal subset. Different from these works, this paper considers jointly optimizing the feature subset and classifier parameters to minimize the number of features and achieve a low classification error rate. Since feature selection is an optimization problem with binary solution space while classifier parameters involve both continuous and discrete variables, our formulated problem becomes a complex multi-objective mixed-variable problem. To address this challenge, we consider a single-objective optimization method and a multi-objective optimization approach. Specifically, in the single-objective optimization method, we adopt the linear weight method to convert our multiple objectives into a fitness function and then propose a mixed-variable grey wolf optimizer (MGWO) to optimize the function. The proposed MGWO introduces Chaos-Faure initialization, Log convergence factor adjustment, and optimal solution adaptive update operators to enhance its adaptability and balance the global and local search of the algorithm. Subsequently, an improved multi-objective grey wolf optimizer (IMOGWO) is introduced to directly address the problem. The proposed IMOGWO introduces improved initialization, local search, and binary variable mutation operators to balance its exploration and exploitation abilities, making it more suitable for our mixed-variable problem. Extensive simulation results show that our MGWO and IMOGWO outperform recent and classic baselines. Moreover, we also find that jointly optimizing classifier parameters can significantly improve classification accuracy.

Selective dynamic band gap tuning in metamaterials using graded photoresponsive resonator arrays.

The introduction of metamaterials has provided new possibilities to manipulate the propagation of waves in different fields of physics, ranging from electromagnetism to acoustics. However, despite the variety of configurations proposed so far, most solutions lack dynamic tunability, i.e. their functionality cannot be altered post-fabrication. Our work overcomes this limitation by employing a photo-responsive polymer to fabricate a simple metamaterial structure and enable tuning of its elastic properties using visible light. The structure of the metamaterial consists of graded resonators in the form of an array of pillars, each giving rise to different resonances and transmission band gaps. Selective laser illumination can then tune the resonances and their frequencies individually or collectively, thus yielding many degrees of freedom in the tunability of the filtered or transmitted wave frequencies, similar to playing a keyboard, where illuminating each pillar corresponds to playing a different note. This concept can be used to realize low-power active devices for elastic wave control, including beam splitters, switches and filters.This article is part of the theme issue 'Current developments in elastic and acoustic metamaterials science (Part 2)'.

Explainable machine learning-based fractional vegetation cover inversion and performance optimization – A case study of an alpine grassland on the Qinghai-Tibet Plateau

Fractional Vegetation Cover (FVC) serves as a crucial indicator in ecological sustainability and climate change monitoring. While machine learning is the primary method for FVC inversion, there are still certain shortcomings in feature selection, hyperparameter tuning, underlying surface heterogeneity, and explainability. Addressing these challenges, this study leveraged extensive FVC field data from the Qinghai-Tibet Plateau. Initially, a feature selection algorithm combining genetic algorithms and XGBoost was proposed. This algorithm was integrated with the Optuna tuning method, forming the GA-OP combination to optimize feature selection and hyperparameter tuning in machine learning. Furthermore, comparative analyses of various machine learning models for FVC inversion in alpine grassland were conducted, followed by an investigation into the impact of the underlying surface heterogeneity on inversion performance using the NDVI Coefficient of Variation (NDVI-CV). Lastly, the SHAP (Shapley Additive exPlanations) method was employed for both global and local interpretations of the optimal model. The results indicated that: (1) GA-OP combination exhibited favorable performance in terms of computational cost and inversion accuracy, with Optuna demonstrating significant potential in hyperparameter tuning. (2) Stacking model achieved optimal performance in FVC inversion for alpine grassland among the seven models (R2 = 0.867, RMSE = 0.12, RPD = 2.552, BIAS = −0.0005, VAR = 0.014), with the performance ranking as follows: Stacking > CatBoost > XGBoost > LightGBM > RFR > KNN > SVR. (3) NDVI-CV enhanced inversion performance and result reliability by excluding data from highly heterogeneous regions that tended to be either overestimated or underestimated. (4) SHAP revealed the decision-making processes of the Stacking and CatBoost models from both global and local perspectives. This allowed for a deeper exploration of the causality between features and targets. This study developed a high-precision FVC inversion scheme, successfully achieving accurate FVC inversion on the Qinghai-Tibet Plateau. The proposed approach provides valuable references for other ecological parameter inversions.

Real-Time Tunable Gas Sensing Platform Based on SnO2 Nanoparticles Activated by Blue Micro-Light-Emitting Diodes

HighlightsBlue micro-light-emitting diodes (μLED)-integrated gas sensors were fabricated as monolithic structure by directly loading sensing materials onto the μLED.SnO2 nanoparticles are activated by blue μLED and exhibit outstanding sensitivity to NO2 at μ-Watt power levels.Noble metal (Au, Pd, Pt)-decorated SnO2 showed the tunable gas selectivity for 4 target gases under blue light illumination.

Efficient predictive modeling of resilient modulus in stabilized clayey soil using automated machine learning

Pavement subgrade design relies on the resilient modulus (Mr) to analyze structural response to vehicle-like loading. Adding stabilizers to the soil subgrade makes estimating Mr difficult and resource-intensive. This study uses an automated machine learning (ML) strategy to predict the Mr of stabilized clayey soil using recycled plastic waste. The proposed method automates model selection and hyperparameter tuning, making it a feasible alternative to tedious ML modeling and costly laboratory testing. From extensive laboratory investigation involving 3285 experimental data points, the automated ML model using Bayesian optimization evaluates ensembles, support vector machine (SVM), neural network (NET), decision trees, (TREE), and Gaussian process (GP) regression models, identifying the best model based on cross-validation mean squared error (MSE). Bayesian optimization explores hyperparameter spaces to find optimal configurations, enhancing the accuracy, scalability, and reliability of the prediction model. The optimization process yielded the best results for the ensemble least square boost (LSBoost) model with a cross-validation mean squared error (MSE) value of 6.723×10−29. The optimized ML model’s performance is measured using R2 and adjusted R2. The LSboost model’s R2 and adjusted R2 values of 0.9999 suggested overfitting, prompting further investigations using performance metrics like root mean squared error (RMSE), mean absolute error (MSE), and probability density function (PDF) for normalized absolute error (NAE) for training and testing datasets for the predictive ML model. The small RMSE and MAE (0.0049 and 0.0005) values and symmetrical NAE distribution of the proposed ML model demonstrate its high accuracy and generalization capabilities. The proposed model was subsequently tested on the new, unseen data and achieved predictions with an error rate of 0.24 %. This confirms the proposed ML model’s superiority over conventional deformation models, making it ideal for reliable geotechnical engineering applications.

A kirigami-based reconfigurable metasurface for selective electromagnetic transmission modulation

Tunable three-dimensional (3D) electromagnetic metasurfaces are essential for achieving selective modulation of polarized waves, but they usually require complex designs and the use of smart materials, posing great implementation challenges. Here, we propose a novel kirigami-based reconfigurable electromagnetic metasurface, which consists of a kirigami-based deformable thin polyimide substrate and periodically arranged copper split-ring resonators. By simple stretch, the two-dimensional (2D) planar metasurface can be uniformly transformed into a 3D state, enabling it to effectively and selectively modulate linearly and circularly polarized waves. Experimental and numerical results reveal the mechanical deformation and transmission characteristics of the metasurface under applied strains. It is shown that the metasurface exhibits good selective transmission tunability while the resonant frequency remains basically unchanged for both transverse electric and transverse magnetic polarized waves. Furthermore, the selective tuning mechanism and the influence of geometrical parameters are also illustrated by the equivalent circuit analysis.

Engineering Single‐Atom Catalysts on Conjugated Porphyrin Polymer Photocatalysts via E‐Waste for Sustainable Photocatalysis

AbstractThis study presents a surface engineering strategy utilizing electronic waste (e‐waste) to incorporate single‐atom catalysts on conjugated polymers. Employing a conjugated porphyrin polymeric photocatalyst, gold single‐atom‐site catalysts are successfully introduced using the acidic metal leachates from e‐waste, where metal speciation and composition are regulated during the metal loading processes. The resulting photocatalyst with gold single atoms demonstrates a remarkable hydrogen peroxide (H2O2) selectivity of up to 97.56%, yielding a pure H2O2 solution at 73.3 µm h−1 under white LED illumination. The produced H2O2 is activated to •OH radicals on the same polymer with mixed gold and iron atoms, enabling a photo‐Fenton reaction and the complete degradation of toxic microcystin‐LR within 10 min under visible light. This study highlights the universal applicability of the metal mining strategy in various photoreactions. It is believed that this discovery pioneers sustainable photocatalysis, allowing the tuning of reactivity and selectivity on photocatalytic surfaces using metal waste.

Copper-Catalyzed Tunable Oxygenative Rearrangement of Tetrahydrocarbazoles.

Herein, we report a general copper-catalyzed method for the tunable oxygenative rearrangement of tetrahydrocarbazoles to cyclopentyl-bearing spiroindolin-2-ones and spiroindolin-3-ones. The method demonstrates excellent chemoselectivity, regioselectivity, and product control simply by using the H2O and O2 as oxygen source, respectively. This open-flask method is safe and simple to operate, and no other chemical oxidants are required. Besides, inspired from the unique pathway of 1, 2-migration rearrangement, a highly controllable hydroxylation of indoles for the construction of C3a-hydroxyl iminium indolines was also developed. Mechanistic experiments suggest that a single-electron transfer-induced oxidation process is responsible for the tunable selectivity control.

Microscale Diffractive Lenses Integrated into Microfluidic Devices for Size-Selective Optical Trapping of Particles.

Integration of optical components into microfluidic devices can enhance particle manipulations, separations, and analyses. We present a method to fabricate microscale diffractive lenses composed of aperiodically spaced concentric rings milled into a thin metal film to precisely position optical tweezers within microfluidic channels. Integrated thin-film microlenses perform the laser focusing required to generate sufficient optical forces to trap particles without significant off-device beam manipulation. Moreover, the ability to trap particles with unfocused laser light allows multiple optical traps to be powered simultaneously by a single input laser. We have optically trapped polystyrene particles with diameters of 0.5, 1, 2, and 4 μm over microlenses fabricated in chromium and gold films. Optical forces generated by these microlenses captured particles traveling at fluid velocities up to 64 μm/s. Quantitative trapping experiments with particles in microfluidic flow demonstrate size-based differential trapping of neutrally buoyant particles where larger particles required a stronger trapping force. The optical forces on these particles are identical to traditional optical traps, but the addition of a continuous viscous drag force from the microfluidic flow introduces tunable size selectivity across a range of laser powers and fluid velocities.