This paper presents a comprehensive investigation on the positive potential of steel slag (SS) to mitigate the autogenous shrinkage of alkali-activated slag (AAS) while maintaining a reasonably high strength. Changes of the physicochemical properties of AAS with the addition of SS were examined in terms of hydration heat, autogenous shrinkage, chemical shrinkage, internal relative humidity (RH) and mechanical behaviors. The microstructure of AAS-SS systems was characterized using X-ray diffraction, thermogravimetric analysis and nitrogen adsorption techniques. The shrinkage mechanism and quantification approach of the AAS-SS systems were discussed, in addition to a sustainability assessment. The results indicate that the 7-day autogenous shrinkage of AAS paste was decreased by 16 %, 35 % and 42 % when SS was incorporated by 15 %, 30 % and 45 % respectively, owing to the obviously slower hydration and higher internal RH at the early age. Meanwhile, the inclusion of SS substantially mitigates the chemical shrinkage and reduces the pores below 50 nm, thereby significantly decreasing the capillary pressure associated with smaller water-filled pore sizes. Substitutions of blast furnace slag by up to 45 % SS enable to reduce CO2 emissions by 18.4 kg/m3 and decrease autogenous shrinkage by 42 % without obvious compromise in the loss of elastic modulus and compressive strength.@en

Vehicle-mounted ground-penetrating radar (GPR) has been used to non-destructively inspect and evaluate railway subgrade conditions. However, existing GPR data processing and interpretation methods mostly rely on time-consuming manual interpretation, and limited studies have applied machine learning methods. GPR data are complex, high-dimensional, and redundant, in particular with non-negligible noises, for which traditional machine learning methods are not effective when applied to GPR data processing and interpretation. To solve this problem, deep learning is more suitable to process large amounts of training data, as well as to perform better data interpretation. In this study, we proposed a novel deep learning method to process GPR data, the CRNN network, which combines convolutional neural networks (CNN) and recurrent neural networks (RNN). The CNN processes raw GPR waveform data from signal channels, and the RNN processes features from multiple channels. The results show that the CRNN network achieves a higher precision at 83.4%, with a recall of 77.3%. Compared to the traditional machine learning method, the CRNN is 5.2 times faster and has a smaller size of 2.6 MB (traditional machine learning method: 104.0 MB). Our research output has demonstrated that the developed deep learning method improves the efficiency and accuracy of railway subgrade condition evaluation.

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This paper presents experimental investigations into the effects of ultrafine blast furnace slag on microstructure improvements against chloride penetration in saturated and unsaturated cementitious systems exposed to cyclic drying–wetting conditions. The hydration kinetics of ultrafine slag powders and pore solution chemistry in slag-blended cementitious systems at different ages, together with the main hydration products and pore structure characteristics, were determined. The chloride profiles accounting for different slag contents and drying–wetting cycles were measured. The results reveal that the reactivity of ultrafine slag can be well described with Avrami’s equation. The dilution effect of the slag predominated the pore solution chemistry, and the pH value decreased with a higher inclusion of slag. An optimal inclusion of 65% slag by mass of the binder corresponding to the finest pore structure and highest hydrotalcite content was found, which provides a reasonable basis for the slow chloride diffusion and high chloride binding. Under drying–wetting exposure, the specimen with a lower saturation exhibited a higher chloride transport caused by capillary absorption in the skin layer. The chloride transport tended to be diffusion controlled after sufficient drying–wetting cycles@en

The influences of liquid-binder ratio and mixing sequence on the performance of superabsorbent polymer (SAP)-containing alkali-activated slag (AAS) mortar are investigated in this study. It is found that the SAP absorbs much less liquid in upper supernatant of AAS than in water. Mixing SAP with liquid first induces a larger absorption capacity of the SAP than mixing it with solid first. Increasing the liquid-binder ratio improves the flowability but reduces the strength of AAS mortar with SAP. Nonetheless, the strength of internally cured mixtures is higher than that of the reference even with an extra liquid-binder ratio of 0.09. The reason behind lies in the refinement of capillary and gel porosity by internal curing, despite the presence of large voids originated from SAP. The autogenous shrinkage of AAS paste is reduced significantly by the incorporation of SAP but the further mitigating effect of increased liquid-binder ratio is limited.

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Internal curing by superabsorbent polymer (SAP) has been applied in alkali-activated slag (AAS) systems by a few previous studies with the purpose to mitigate the autogenous shrinkage. However, the effects of SAP on other properties of AAS have been rarely studied. In this paper, the workability, strength, permeability, and frost resistance of AAS mortar with synthesized SAP are investigated besides the autogenous shrinkage. Two SAP introducing ways (dry mixing and wet mixing) are considered. It is found that the flowability of AAS mortar decreases with the increase of SAP dosage regardless of the introducing way. The strength and permeability increase with the SAP dosage when it is below a certain amount depending on the mixing way. The autogenous shrinkage can be mitigated significantly by the incorporation of SAP and the mitigating effect is more pronounced by wet mixing. The frost resistance becomes better when more SAP is introduced in either way. The mechanisms behind these phenomena are explained based on the characterization results on the reaction kinetics, reaction products and pore structure of the mixtures with SAP.

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Several prokaryotic Argonaute proteins (pAgos) utilize small DNA guides to mediate host defense by targeting invading DNA complementary to the DNA guide. It is unknown how these DNA guides are being generated and loaded onto pAgo. Here, we demonstrate that guide-free Argonaute from Thermus thermophilus (TtAgo) can degrade double-stranded DNA (dsDNA), thereby generating small dsDNA fragments that subsequently are loaded onto TtAgo. Combining single-molecule fluorescence, molecular dynamic simulations, and structural studies, we show that TtAgo loads dsDNA molecules with a preference toward a deoxyguanosine on the passenger strand at the position opposite to the 5′ end of the guide strand. This explains why in vivo TtAgo is preferentially loaded with guides with a 5′ end deoxycytidine. Our data demonstrate that TtAgo can independently generate and selectively load functional DNA guides.

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