Linear Transfer Research Articles

Synthesis, characterization, and corrosion protection potential of a highly effective and water‐miscible surfactant for mild steel in sulfuric acid solution

AbstractIn the current study, a tetracationic quaternary ammonium salt (TCQAC) was synthesized and characterized and its ability to suppress corrosion on mild steel (MS) in a 0.5 M H2SO4 solution was examined. Various chemical, electrochemical, and surface characterization techniques were utilized to study the inhibition efficiency of TCQAC. The TCQAC manifests 99.83% efficiency at 20 ppm concentration. Out of all the examined isotherm models, the Langmuir isotherm offered the best fit for the TCQAC adsorption on the MS surface. A very high negative value of ΔGads (−45.18 kJ mol−1) suggests that the adsorption of TCQAC followed the chemisorption mechanism. Electrochemical studies indicate that TCQAC increases the linear and charge transfer resistances (LPR and Rct, respectively). TCQAC slows down the anodic and cathodic Tafel reactions; however, it acts as an anodic‐type inhibitor at 5, 10, and 20 ppm. The appearance of extra Cl and N signals in the energy dispersive x‐ray (EDX) spectrum and an improvement in surface smoothness in the scanning electron microscope (SEM) image of the inhibited sample corroborated the adsorption method of corrosion inhibition. X‐ray photoelectron spectroscopy (XPS) study indicates that TCQAC creates corrosion preventive layers by chemical adsorption. In Frontier molecular orbitals (FMOs), highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) were delocalized around the central part that comprises two benzyls, four allyls, and one hydrocarbon ((CH2)6) moieties and two quaternary nitrogen atoms. The outcomes of XPS and density functional theory (DFT) analyses indicate that the chemisorption of TCQAC occurs by dπ–pπ bonding with the surface iron atoms. The π‐electrons of aryl and allyl moieties extensively participate in the bonding.

Thermal and electrical properties of photovoltaic cell with linear phenomenological heat transfer law

Abstract The thermal and electrical properties of photovoltaic cell (PVC) under linear phenomenological heat transfer law between it and the environment is studied through finite time thermodynamics and the volt-ampere characteristic equation. The properties of PVC are affected by heat transfer between PVC and environment. There are optimal solar radiation intensity and PVC output voltage (OV), which make the photoelectric conversion efficiency (PECE) of PVC reach the highest value. When OV and solar radiation intensity are 28.50 V and 700 W/m2, the maximum PECE is 0.156. There is also the best solar radiation intensity, which makes the open-circuit voltage (OCV) reach the maximum. When solar radiant intensity is 669 W/m2, the maximum OCV is 33.14 V. The values of power output and short-circuit current (SCC) are monotonically increasing with solar radiation intensity. Given solar radiation intensity, the power output and OV exhibit a parabolic shape. The operating temperature falls first and then grows with the OV. However, the change of operating temperature with OV is not much. Band gap is a decreasing function of operating temperature. This article can give theoretical support for the design and use of PVCs.

Nanostructure design of 3D printed materials through macromolecular architecture.

Polymerization-induced microphase separation (PIMS) has been previously combined with 3D printing to develop customized nanostructured materials with a wide range of functional applications. In traditional PIMS, monofunctional, linear macromolecular chain transfer agents (macroCTAs) are used to develop macroCTA-b-P(monomer-stat-crosslinker) networks that self-assemble into unique disordered nanostructures. In this work, we designed a significantly altered network structure by utilizing linear macroCTAs with pendant CTA groups, which provides a novel network upon polymerization (i.e., branched copolymers, [macroCTA-graft-[P(monomer-stat-crosslinker)] n ]-b-P(monomer-stat-crosslinker)). Intriguingly, this method leads to the development of alternative disordered morphologies where the internal nanostructure can be precisely controlled. By systematically varying the number of pendant CTA groups, we demonstrate controlled transitions in macroCTA domain continuity, nanodomain size, and phase interface sharpness. These tunable properties translate to adjustable mechanical and swelling behaviors in the resulting 3D printed objects, ultimately enabling the fabrication of smart 4D materials (swelling-induced actuators and temperature-responsive shape-morphing objects). This research significantly expands the design toolbox for 3D printed PIMS materials, providing increased flexibility in the development of advanced materials with specific nanostructures and functionalities.

Modeling of speech-dependent own voice transfer characteristics for hearables with an in-ear microphone

Many hearables contain an in-ear microphone, which may be used to capture the own voice of its user. However, due to the hearable occluding the ear canal, the in-ear microphone mostly records body-conducted speech, typically suffering from band-limitation effects and amplification at low frequencies. Since the occlusion effect is determined by the ratio between the air-conducted and body-conducted components of own voice, the own voice transfer characteristics between the outer face of the hearable and the in-ear microphone depend on the speech content and the individual talker. In this paper, we propose a speech-dependent model of the own voice transfer characteristics based on phoneme recognition, assuming a linear time-invariant relative transfer function for each phoneme. We consider both individual models and models averaged over several talkers. Experimental results based on recordings with a prototype hearable show that the proposed speech-dependent model enables to simulate in-ear signals more accurately than a speech-independent model in terms of technical measures, especially under utterance mismatch and talker mismatch. Additionally, simulation results show that talker-averaged models generalize better to different talkers than individual models.

Efficient N2 electroreduction enabled by linear charge transfer over atomically dispersed W sites.

Electrocatalytic nitrogen reduction reaction (NRR) presents a sustainable alternative to the Haber-Bosch process for ammonia (NH3) production. However, developing efficient catalysts for NRR and deeply elucidating their catalytic mechanism remain daunting challenges. Herein, we pioneered the successful embedding of atomically dispersed (single/dual) W atoms into V2-x CT y via a self-capture method, and subsequently uncovered a quantifiable relationship between charge transfer and NRR performance. The prepared n-W/V2-x CT y shows an exceptional NH3 yield of 121.8 μg h-1 mg-1 and a high faradaic efficiency (FE) of 34.2% at -0.1 V (versus reversible hydrogen electrode (RHE)), creating a new record at this potential. Density functional theory (DFT) computations reveal that neighboring W atoms synergistically collaborate to significantly lower the energy barrier, achieving a remarkable limiting potential (U L) of 0.32 V. Notably, the calculated U L values for the constructed model show a well-defined linear relationship with integrated-crystal orbital Hamilton population (ICOHP) (y = 0.0934x + 1.0007, R 2 = 0.9889), providing a feasible activity descriptor. Furthermore, electronic property calculations suggest that the NRR activity is rooted in d-2π* coupling, which can be explained by the "donation and back-donation" hypothesis. This work not only designs efficient atomic catalysts for NRR, but also sheds new insights into the role of neighboring single atoms in improving reaction kinetics.

Prediction of River Water Treatment Plant Operational Performances using Optimization Approach in Artificial Neural Network Model

Uncontrolled development at the upstream of the river catchment have led to detrimental effect to the environment, including degradation of river water quality. River Water Treatment Plant (RWTP) technology was introduced to reduce the contamination loading into the river water system, worldwide. The technology offers the best biological treatment process including simplicity and stable removal efficiency. However, the plant performance plan is difficult task to predict, thus might have influence the operational control. Recently, artificial neural network (ANN) models have been widely applied in environmental engineering area due to the ability to skip the complexity process to assume of the unknown variables compare to conventional physical based model. In this study, the results of 3-yrs performance using ANN of RWTP were developed. Feed-forward back-propagation using Levenberg–Marquardt (trainlm) used as for this predictive approach. The ideal configuration involves utilizing the tangent sigmoid transfer function (Tansig) in the hidden layer and a linear transfer function (Purelin) in the output layer, with 25 neurons. This configuration yields an R2 value of 0.963 and the most least mean square error (MSE) of 30.39. From the comparison between two model (bio-kinetic and ANN), performance indicator for ANN model shows the best and the most optimum model. Ultimately, RWTP optimization using black-box model ANN is more reliable and timesaving as compared to conventional bio-kinetic model. The development of the proposed model can be implemented and used for various water quality improvement facilities and predict the effluent target parameter in RWTP with higher degree of accuracy.

IAPCP: An Effective Cross‐Project Defect Prediction Model via Intra‐Domain Alignment and Programming‐Based Distribution Adaptation

Cross‐project defect prediction (CPDP) aims to identify defect‐prone software instances in one project (target) using historical data collected from other software projects (source), which can help maintainers allocate limited testing resources reasonably. Unfortunately, the feature distribution discrepancy between the source and target projects makes it challenging to transfer the matching feature representation and severely hinders CPDP performance. Besides, existing CPDP models require an intensively expensive and time‐consuming process to tune a lot of parameters. To address the above limitations, we propose an effective CPDP model named IAPCP based on distribution adaptation in this study, which consists of two stages: correlation alignment and intra‐domain programming. Correlation alignment first calculates the covariance matrices of the source and target projects and then erases some features of the source project (i.e., whitening operation) and employs the features of the target project (i.e., target covariance) to fill the source project, thereby well aligning the source and target feature distributions and reducing the distribution discrepancy across projects. Intra‐domain programming can directly learn a nonparametric linear transfer defect predictor with strong discriminative capacity by solving a probabilistic annotation matrix (PAM) based on the adjusted features of the source project. The model does not require model selection and parameter tuning. Extensive experiments on a total of 82 cross‐project pairs from 16 software projects demonstrate that IAPCP can achieve competitive CPDP effectiveness and efficiency compared with multiple state‐of‐the‐art baseline models.

Bispectral analysis and simulation modeling of quadratic nonlinear system with specific turbulent-fluctuation-excitation-response types

There exists a kind of quadratic nonlinear system with specific type of turbulent fluctuation excitation in nature, which belongs to a special non-Gaussian input signal system. Its characteristic is that the input signal spectrum is generated by turbulent fluctuations, and the power spectrum distribution of this turbulence fluctuation signal is close to Gaussian distribution. Starting with the work of Choi et al. (<ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://doi.org/10.1016/0022-460X(85)90370-0">1985 <i>J. Sound Vib</i><i>.</i> <b>99</b> 309</ext-link>) and Kim et al. [<ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://ieeexplore.ieee.org/document/1145286">1987 <i>IEEE J. Ocean. Eng.</i> <b>OE-12</b> 568</ext-link>), we extend the simulation of a specific turbulent fluctuation excited response-type quadratic nonlinear system represented by the wave excited mooring ship response, and fully develop the internal development of turbulence based on bispectral analysis technology. We also extend the simulation system and conduct systematic modeling analysis. The complete iterative method [<ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://doi.org/10.1088/1402-4896/ab725f">2020 <i>Phys. Scr.</i> <b>95</b> 055202</ext-link>] is used to solve the model, and calculate the linear transfer function and quadratic nonlinear transfer function. The comparison of simulation and modeling results with the real systems and their models confirms the correctness of the results from system simulation, system modeling, and model solving. The results obtained are all in line with expectations. The coherence analysis shows that the quadratic coherence of the random wave-ship swaying system is much greater than the linear coherence, but the linear coherence of the fully developed turbulence is greater for the near Gaussian input type. The reverse computation verification or comparison with real systems indicates that the turbulence simulation and system modeling method in this work have good accuracy and high efficiency in solving algorithms, and the research results can be effectively applied to the model description and system analysis of the quadratic nonlinear systems related to specific turbulent fluctuation excitation response.

Comparative analyses of Nonlinear Optical (NLO) properties, structural characterization and density functional theory of chloro-trifluoromethyl moiety chalcone derivatives for optical limiting application

This study focuses on the impact of substitutional positions on the nonlinear optical (NLO) and photophysical properties of four novel chalcone derivatives (I–IV). The grown crystals were characterized using FT-IR, NMR and single crystal X-ray diffraction (SCXRD) techniques, and the intermolecular interactions were studied by examining the unique packing motifs. The linear and 3rd-order NLO properties were experimentally determined using UV–Vis spectrometer and Z-scan setup with 637 nm continuous wave excitation, respectively. Compounds I–IV were studied theoretically using density functional theory (DFT) to support the experimental results. B3LYP functional was used to optimize the geometries, and the molecular properties of the compounds were determined via molecular electrostatic potential (MEP), natural population analysis (NPA), and localized orbital locator-π (LOL-π) analyze. The photophysical properties were analyzed using time-dependent DFT (TDDFT) using both CAM-B3LYP and B3LYP functionals to investigate the impact of the long-range correction. Inter-fragment charge transfer (IFCT) analysis and hole-electron analysis were employed to examine the linear optical properties and charge transfer mechanism. The NLO properties such as dipole moment (μ), polarizability (α), first and second hyperpolarizabilities (β and γ) were examined with the numerically differentiate analytic method using both functionals. Z-scan NLO data shows that the nonlinear refraction (NLR) coefficient of I–IV is significantly influenced by intermolecular interactions, specifically aggregation phenomena. Compound IV exhibits an exceptional nonlinear absorption (NLA) response and superior optical limiting properties compared to others attributed to its para-substituted strong electron-acceptor group (CF3) which results in highest transition dipole moment, lowest excitation energy, and well-defined charge transfer processes within its molecule. As a result, this suggests that it could be a potential candidate for NLO applications.

An adaptive fractional order controller design: A realization for liquid level regulation in liquid level plant

In this paper, an adaptive FO − IλD1−λ controller design is proposed using a frequency domain approach. The proposed adaptive controller parameters are tuned based on the frequency domain specifications. To realize the velocity error constant for the proposed controller, Oustaloup Recursive Approximation (ORA) method is employed. The proposed controller is realized for liquid level regulation in the Liquid Level Plant (LLP). An identification technique is proposed to identify a linear adaptable parameter transfer function (LAPTF) model of LLP. The performance of the proposed controller is compared with the existing works. It is observed that the proposed controller outperforms the existing controllers in the literature.

Analytical modeling and analysis of the low-velocity penetration and non-penetration behaviors of fiber-reinforced composite cylindrical shells based on critical impact velocity criterion

An analytical model is developed to predict the impact characteristic parameters of composite cylindrical shells when low-velocity penetration and non-penetration damage are considered. The critical impact velocity criterion for judging the penetrating failure of such structures is also proposed. The impact contact force, displacement, and load-displacement curve of the structure subjected to oblique non-penetrating impact loading are solved using the improved Hertz contact theory, cumulative damage theory, stiffness degradation method, etc. Based on the formula derivation of critical impact velocity, the key impact parameters are solved, such as the residual velocity of the projectile and damage area of the structure with penetrating impact, which takes into account three energy absorption mechanisms, such as delamination, laminating crushing and linear momentum transfer. A series of non-penetrating and penetrating impact tests are conducted using a low-velocity drop hammer rig and a light gas gun. The prediction results of the proposed model are compared to both experimental and literature results. One can find out that the largest prediction errors of the peaks of impact contact force and displacement deformation are 12.3 and 9.2 %, respectively, and the counterparts of residual velocity and damage area are 12.0 and 6.2 %, respectively. Hence, the present model is trustworthy for predicting these impact parameters. Moreover, it is advised to adopt an appropriate stacking sequence, a high ratio of structural thickness to radius, a large oblique impact angle of the projectile, and a small mass ratio of the projectile to such a structure to enhance its impact resistance or reduce impact response. In addition, it is worth noting that the current model probably has a big prediction error of the high-velocity impact behaviors involving the material heating and soft effects caused by penetration damage since high strain rate and temperature effects are not considered.

The sensitivity of Southern Ocean atmospheric dimethyl sulfide (DMS) to modeled oceanic DMS concentrations and emissions

Abstract. The biogeochemical formation of dimethyl sulfide (DMS) from the Southern Ocean is complex, dynamic, and driven by physical, chemical, and biological processes. Such processes, produced by marine biogenic activity, are the dominant source of sulfate aerosol over the Southern Ocean. Using an atmosphere-only configuration of the United Kingdom Earth System Model (UKESM1-AMIP), we performed eight 10-year simulations for the recent past (2009–2018) during austral summer. We tested the sensitivity of atmospheric DMS to four oceanic DMS datasets and three DMS transfer velocity parameterizations. One oceanic DMS dataset was developed here from satellite chlorophyll a. We find that the choice of oceanic DMS dataset has a larger influence on atmospheric DMS than the choice of DMS transfer velocity. Simulations with linear transfer velocity parameterizations show a more accurate representation of atmospheric DMS concentration than those using quadratic relationships. This work highlights that the oceanic DMS and DMS transfer velocity parameterizations currently used in climate models are poorly constrained for the Southern Ocean region. Simulations using oceanic DMS derived from satellite chlorophyll a data, and when combined with a recently developed linear transfer velocity parameterization for DMS, show better spatial variability than the UKESM1 configuration. We also demonstrate that capturing large-scale spatial variability can be more important than large-scale interannual variability. We recommend that models use a DMS transfer velocity parameterization that was developed specifically for DMS and improvements to oceanic DMS spatial variability. Such improvements may provide a more accurate process-based representation of oceanic and atmospheric DMS, and therefore sulfate aerosol, in the Southern Ocean region.

Input-size dependence of the baroreflex neural arc transfer characteristics during Gaussian white noise inputs.

Although Gaussian white noise (GWN) inputs offer a theoretical framework for identifying higher-order nonlinearity, an actual application to the data of the neural arc of the carotid sinus baroreflex did not succeed in fully predicting the well-known sigmoidal nonlinearity. In the present study, we assumed that the neural arc can be approximated by a cascade of a linear dynamic (LD) component and a nonlinear static (NS) component. We analyzed the data obtained using GWN inputs with a mean of 120 mmHg and standard deviations (SDs) of 10, 20, and 30 mmHg for 15 min each in anesthetized rats (n = 7). We first estimated the linear transfer function from carotid sinus pressure to sympathetic nerve activity (SNA) and then plotted the measured SNA against the linearly predicted SNA. The predicted and measured data pairs exhibited an inverse sigmoidal distribution when grouped into 10 bins based on the size of the linearly predicted SNA. The sigmoidal nonlinearity estimated via the LD-NS model showed a midpoint pressure (104.1 ± 4.4 mmHg for SD of 30 mmHg) lower than that estimated by a conventional stepwise input (135.8 ± 3.9 mmHg, P < 0.001). This suggests that the NS component is more likely to reflect the nonlinearity observed during pulsatile inputs that are physiological to baroreceptors. Furthermore, the LD-NS model yielded higher R2 values compared with the linear model and the previously suggested second-order Uryson model in the testing dataset.NEW & NOTEWORTHY We examined the input-size dependence of the baroreflex neural arc transfer characteristics during Gaussian white noise inputs. A linear dynamic-static nonlinear model yielded higher R2 values compared with a linear model and captured the well-known sigmoidal nonlinearity of the neural arc, indicating that the nonlinear dynamics contributed to determining sympathetic nerve activity. Ignoring such nonlinear dynamics might reduce our ability to explain underlying physiology and significantly limit the interpretation of experimental data.

Infrared thermography application for in-situ determination of the building envelope thermal performance

During the phase of creating energy audit documentation, many calculations are needed when evaluating the thermal performance of building envelope components due to a large number of distinct components and junctions. Several computer programs are available to assess buildings’ energy consumption, using the thermal performance of building envelope elements as input data. These thermal performances are often provided with varying degrees of accuracy, typically only for the current field of the component, without considering the impact of thermal bridges. However, when using linear heat transfer coefficients ψ, the details provided in thermal bridges’ atlases do not cover all the case studies encountered in the current design, which often requires approximate details. In some cases, the technical documentation of the building is unavailable, which leads to various assumptions about the detailing of the building envelope and a non-realistic picture of the energy performance of the assessed building. The paper presents a methodology and accompanying software called THERMOG that evaluates the thermal performance of the building envelope under actual operating conditions using aerial and terrestrial thermography methods. The paper presents initial findings from some case studies used to calibrate the developed methodology.

Automated Data–Driven Model Extraction and Validation of Inverter Dynamics with Grid Support Function

This research focuses on the evolving dynamics of the power grid, where traditional synchronous generators are being replaced by non-synchronous power electronic converter (PEC)-interfaced renewable energy sources. The non-linear dynamics must be accurately modeled to ensure the stability of future converter-dominated power systems (CDPS). However, obtaining comprehensive dynamic models becomes more complex and computationally intensive as the system grows. This study proposes a scalable and automated data-driven partitioned modeling framework for CDPS dynamics. The method constructs reduced-ordered dynamic linear transfer function models using input-output measurements from a PEC switching model. Validation experiments were conducted on single-house and multi-house scenarios, demonstrating high accuracy (over 97%) and significant computational speed improvements (6.5 times faster) compared to comprehensive models. This framework and modeling approach offer valuable insights for efficient analysis of power system dynamics, aiding in planning, operation, and dispatch.

Alternating Transfer Functions to Prevent Overfitting in Non-Linear Regression with Neural Networks

ABSTRACT In nonlinear regression with machine learning methods, neural networks (NNs) are ideally suited due to their universal approximation property, which states that arbitrary nonlinear functions can thereby be approximated arbitrarily well. Unfortunately, this property also poses the problem that data points with measurement errors can be approximated too well and unknown parameter subspaces in the estimation can deviate far from the actual value (so-called overfitting). Various developed methods aim to reduce overfitting through modifications in several areas of the training. In this work, we pursue the question of how an NN behaves in training with respect to overfitting when linear and nonlinear transfer functions (TF) are alternated in different hidden layers (HL). The presented approach is applied to a generated dataset and contrasted to established methods from the literature, both individually and in combination. Comparable results are obtained, whereby the common use of purely nonlinear transfer functions proves to be not recommended generally.

Real-time gypsum quality estimation in an industrial calciner: A neural network-based approach

Total bound moisture (TBM) is a typical quality indicator of industrial-grade gypsum. This gypsum is comprised of three distinct phases, namely anhydrite, dihydrate, and hemihydrate, of which only the latter is of much industrial use. TBM analysis is a lengthy laboratory procedure, and an artificial neural network (ANN) TBM inference measurement is proposed as a fast and online alternative. An ANN inference model for gypsum TBM based on plant data was developed. The inputs to the network were primarily focused on the plant's calciner, and different network topologies, data divisions, and transfer functions were investigated. Furthermore, the applicability of the TBM value as a quality indicator was investigated based on a gypsum phase analysis. A strong correlation between TBM and the gypsum hemihydrate and anhydrite content was found, validating the plant target TBM of 5.8% as a quality indicator. A network topology consisting of one hidden layer with logarithmic-sigmoid (logsig) and pure linear (purelin) transfer functions showed the best performance (R &gt; 90%).

Adjustment of light-responsive NADP dynamics in chloroplasts by stromal pH

Cyclic electron transfer (CET) predominates when NADP+ is at basal levels, early in photosynthetic induction; however, the mechanism underlying the subsequent supply of NADP+ to fully drive steady-state linear electron transfer remains unclear. Here, we investigated whether CET is involved in de novo NADP+ supply in Arabidopsis thaliana and measured chloroplastic NADP dynamics to evaluate responsiveness to variable light, photochemical inhibitors, darkness, and CET activity. The sum of oxidized and reduced forms shows that levels of NADP and NAD increase and decrease, respectively, in response to light; levels of NADP and NAD decrease and increase in the dark, respectively. Moreover, consistent with the pH change in the stroma, the pH preference of chloroplast NAD+ phosphorylation and NADP+ dephosphorylation is alkaline and weakly acidic, respectively. Furthermore, CET is correlated with upregulation of light-responsive NADP level increases and downregulation of dark-responsive NADP level reductions. These findings are consistent with CET helping to regulate NADP pool size via stromal pH regulation under fluctuating light conditions.

Transfer learning across datasets with different input dimensions: An algorithm and analysis for the linear regression case

With the development of new sensors and monitoring devices, more sources of data become available to be used as inputs for machine learning models. These can on the one hand help to improve the accuracy of a model. On the other hand, combining these new inputs with historical data remains a challenge that has not yet been studied in enough detail. In this work, we propose a transfer learning algorithm that combines new and historical data with different input dimensions. This approach is easy to implement, efficient, with computational complexity equivalent to the ordinary least-squares method, and requires no hyperparameter tuning, making it straightforward to apply when the new data is limited. Different from other approaches, we provide a rigorous theoretical study of its robustness, showing that it cannot be outperformed by a baseline that utilizes only the new data. Our approach achieves state-of-the-art performance on 9 real-life datasets, outperforming the linear DSFT, another linear transfer learning algorithm, and performing comparably to non-linear DSFT.11The code for reproducing the results of this paper is available at https://github.com/lpsilvestrin/incremental_input_tl.

3D printed indirect bonding trays: Transfer accuracy of hard versus soft resin material in a prospective, randomized, single-blinded clinical study.

This prospective clinical study aimed to compare transfer accuracy and immediate loss rate of hard versus soft transfer trays utilizing a CAD/CAM workflow. We performed virtual bracket placement on intraoral scans of adolescent patients to create individual indirect bonding trays. Orthodontic software (Appliance Designer, 3Shape, Copenhagen, Denmark) was used to design the trays, which were then produced using 3D printing technology. Patients were randomly assigned to the hard or soft resin groups with a 1:1 allocation. Subgroups were determined based on the Little's Irregularity Index and distributed equally. 552 brackets were bonded onto adolescent patients using 46 CAD/CAM indirect bonding trays. The linear mean transfer errors ranged from -0.011mm (soft) to -0.162mm (hard) and angularly -0.255° (hard) and -0.243° (soft). No statistically significant differences were found between the subgroups or soft and hard resin groups. However, the transfer accuracy of molar brackets was significantly lower in the transversal and horizontal directions. All mean transfer errors were within the limits of clinical acceptability. The loss rate was 2.4 % in the hard resin group and 2.3 % in the soft resin group. The Intra Observer Correlation was excellent. CAD/CAM technology for indirect bracket bonding has been proven reliable in a randomized clinical trial. Both hard and soft resin showed a low rate of immediate loss compared to the current literature. Soft resin was more favorable than hard resin in terms of accuracy and usability. However, the indirect bonding of molar brackets is significantly less accurate than incisor brackets.