Search for: All records

A catalytic surface should be stable under reaction conditions to be effective. However, it takes significant effort to screen many surfaces for their stability, as this requires intensive quantum chemical calculations. To more efficiently estimate stability, we provide a general and data-efficient machine learning (ML) approach to accurately and efficiently predict the surface energies of metal alloy surfaces. Our ML approach introduces an element-centered fingerprint (ECFP) which was used as a vector representation for fitting models for predicting surface formation energies. The ECFP is significantly more accurate than several existing feature sets when applied to dilute alloy surfaces and is competitive with existing feature sets when applied to bulk alloy surfaces or gas-phase molecules. Models using the ECFP as input can be quite general, as we created models with good accuracy over a broad set of bimetallic surfaces including most d-block metals, even with relatively small datasets. For example, using the ECFP, we developed a kernel ridge regression ML model which is able to predict the surface energies of alloys of diverse metal combinations with a mean absolute error of 0.017 eV atom⁻¹. Combining this model with an existing model for predicting adsorption energies, we estimated segregation trends of 596 single-atom alloys (SAAs)with and without CO adsorbed on these surfaces. As a simple test of the approach, we identify specific cases where CO does not induce segregation in these SAAs.

 

This paper examined the effect of Si addition on the cracking resistance of Inconel 939 alloy after laser additive manufacturing (AM) process. With the help of CALculation of PHAse Diagrams (CALPHAD) software Thermo-Calc, the amounts of specific elements (C, B, and Zr) in liquid phase during solidification, cracking susceptibility coefficients (CSC) and cracking criterion based on$$\left| {{\text{d}}T/{\text{d}}f_{{\text{s}}}^{1/2} } \right|$$$\left(\text{d}T/\text{d}{f}_{\text{s}}^{1/2}\right)$values (T: solidification temperature,f_s: mass fraction of solid during solidification) were evaluated as the indicators for composition optimization. It was found that CSC together with$$\left| {{\text{d}}T/{\text{d}}f_{{\text{s}}}^{1/2} } \right|$$$\left(\text{d}T/\text{d}{f}_{\text{s}}^{1/2}\right)$values provided a better prediction for cracking resistance.

Graphical abstract

 

Dissociation of CO₂on iron clusters was studied by using semilocal density functional theory and basis sets of triple‐zeta quality. Fe₂, Fe₄, and Fe₁₆clusters were selected as the representative host clusters. When searching for isomers of Fe_nCO₂,n=2, 4 and 16 corresponding to carbon dioxide attachment to the host clusters, its reduction to O and CO, and to the complete dissociation, it was found that the total spin magnetic moments of the lowest energy states of the isomers are often quenched with respect to those of initial reagents Fe_n+CO₂. Dissociation pathways of the Fe₂+CO₂, Fe₄+CO₂, and Fe₁₆+CO₂reactions contain several transition states separated by the local minima states; therefore, a natural question is where do the spin flips occur? Since lifetimes of magnetically excited states were shown to be of the order of 100 fs, the search for the CO₂dissociation pathways was performed under the assumption that magnetic deexcitation may occur at the intermediate local minima. Two dissociation pathways were obtained for each Fe_n+CO₂reaction using the gradient‐based methods. It was found that the Fe₂+CO₂reaction is endothermic with respect to both reduction and complete dissociation of CO₂, whereas the Fe₄+CO₂and Fe₁₆+CO₂reactions are exothermic to both reduction and complete dissociation of carbon dioxide. The CO₂reduction was found to be more favorable than its complete dissociation in the Fe₄case.

 

Formulations containing vinyl ethers and epoxy were successfully polymerized through a radical‐induced cationic frontal polymerization mechanism, using an iodonium salt superacid generator with a peroxide thermal radical initiator and fumed silica as a filler. It was found that an increase of vinyl ether content resulted in higher front velocities for divinyl ethers in formulations with trimethylolpropane triglycidyl ether. However, increased hydroxymonovinyl ether either decreased the front velocity or suppressed frontal polymerization. The kinetic effects of the superacid generator and thermal radical initiator with varying vinyl ether content were also studied. It was observed that increasing concentrations of initiators increased the front velocity, with the system exhibiting higher sensitivity to the superacid generator concentration.

 

Our mother nature has been providing human beings with numerous resources to inspire from, in building a finer life. Particularly in structural design, plenteous notions are being drawn from nature in enhancing the structural capacity as well as the appearance of the structures. Here plant stems, roots and various other structures available in nature that exhibit better buckling resistance are mimicked and modeled by finite element analysis to create a training database. The finite element analysis is validated by uniaxial compression to buckling of 3D printed biomimetic rods using a polymeric ink. After feature identification, forward design and data filtering are conducted by machine learning to optimize the biomimetic rods. The results show that the machine learning designed rods have 150% better buckling resistance than all the rods in the training database, i.e., better than the nature’s counterparts. It is expected that this study opens up a new opportunity to design engineering rods or columns with superior buckling resistance such as in bridges, buildings, and truss structures.

 

Tension programmed shape memory polymer (SMP) fibers have been used as sutures for closing wide‐opened cracks per the close‐then‐heal strategy. However, the composite may be subjected to compression loading during service. These compression loads can reduce the amount of recoverable strain in these pre‐tensioned fibers, limiting their ability to close cracks. The purpose of this study is to investigate the effect of in‐service compression loading on the shape memory effect (SME) of composites consisting of SMP fiber and SMP matrix. To this end, pre‐stretched shape memory Polyethylene Terephthalate (PET) fibers were embedded into a shape memory vitrimer to obtain composite samples with different fiber volume fractions. The SME of both the PET fiber and the vitrimer was investigated. The effect of compression load on the SME of the composite was studied. It is found that, uniaxial compression on the composite along the fiber direction significantly reduced the shrinking ability of the embedded pre‐tensioned SMP fibers. Hence, this is a factor that needs to be considered when designing such types of self‐healing composites.

 

In various scenarios from system login to writing emails, documents, and forms, keyboard inputs carry alluring data such as passwords, addresses, and IDs. Due to commonly existing non-alphabetic inputs, punctuation, and typos, users' natural inputs rarely contain only constrained, purely alphabetic keys/words. This work studies how to reveal unconstrained keyboard inputs using auditory interfaces. Audio interfaces are not intended to have the capability of light sensors such as cameras to identify compactly located keys. Our analysis shows that effectively distinguishing the keys can require a fine localization precision level of keystroke sounds close to the range of microseconds. This work (1) explores the limits of audio interfaces to distinguish keystrokes, (2) proposes a μs-level customized signal processing and analysis-based keystroke tracking approach that takes into account the mechanical physics and imperfect measuring of keystroke sounds, (3) develops the first acoustic side-channel attack study on unconstrained keyboard inputs that are not purely alphabetic keys/words and do not necessarily follow known sequences in a given dictionary or training dataset, and (4) reveals the threats of non-line-of-sight keystroke sound tracking. Our results indicate that, without relying on vision sensors, attacks using limited-resolution audio interfaces can reveal unconstrained inputs from the keyboard with a fairly sharp and bendable "auditory eyesight." 

Electroencephalography (EEG) based systems utilize machine learning (ML) and deep learning (DL) models in various applications such as seizure detection, emotion recognition, cognitive workload estimation, and brain-computer interface (BCI). However, the security and robustness of such intelligent systems under analog-domain threats have received limited attention. This paper presents the first demonstration of physical signal injection attacks on ML and DL models utilizing EEG data. We investigate how an adversary can degrade the performance of different models by non-invasively injecting signals into EEG recordings. We show that the attacks can mislead or manipulate the models and diminish the reliability of EEG-based systems. Overall, this research sheds light on the need for more trustworthy physiological-signal-based intelligent systems in the healthcare field and opens up avenues for future work. 

As a new format of mobile application, mini-programs, which function within a larger app and are built with HTML, CSS, and JavaScript web technology, have become the way to do almost everything in China. Many researchers have done the ecosystem or developing study, while the permission problem has not been investigated yet. In this paper, we present our studies on the permission management of mini-programs and conduct a systematic study on 9 popular mobile host app ecosystems that host over 7 million mini-programs. After testing over 2,580 APIs, we extracted a common abstract model for mini-programs’ permission control and revealed six categories of potential security vulnerabilities due to improper permission management. It is alarming that the current popular mobile app ecosystems (i.e., host apps) under study have at least one security vulnerability due to the mini-programs’ improper permission management. We present the corresponding attack methods to dissect these potential weaknesses further to exploit the discovered vulnerabilities. To prove that the revealed vulnerabilities may cause severe consequences in real-world use, we show three kinds of attacks without privileges or cracking the host apps. We have responsibly disclosed the newly discovered vulnerabilities, and two CVEs were issued. Finally, we put forward systematic suggestions to strengthen the standardization of mini-programs.