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arxiv: 1906.08461 · v1 · pith:XPYVQLWLnew · submitted 2019-06-20 · ⚛️ physics.app-ph

Micro/nanomaterials for improving solar still and solar evaporation -- A review

Guilong Peng , Swellam W. Sharshir , Yunpeng Wang , Meng An , A.E. Kabeel , Jianfeng Zang , Lifa Zhang , Nuo Yang This is my paper

Pith reviewed 2026-05-25 19:17 UTC · model grok-4.3

classification ⚛️ physics.app-ph
keywords solar stillsolar evaporationmicro/nanomaterialsdesalinationmachine learningthermal designevaporation efficiencysolar desalination
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The pith

Micro and nanomaterials can boost solar still efficiency by optimizing evaporation, with machine learning identifying thermal design as the dominant factor.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper reviews how micro and nanomaterials, along with system designs, are used to improve solar evaporation and overcome limits in traditional solar stills for desalination. It covers recent materials and configurations that raise performance. Machine learning analysis of the literature ranks thermal design as the most important contributor to high efficiency. The review also flags open issues such as air-water interface temperature, salt rejection, durability, and how solid-liquid interactions affect phase change. A sympathetic reader would see this as evidence that material and thermal choices can make solar desalination more practical.

Core claim

Optimizing the solar evaporation process based on micro/nanomaterials is a promising strategy to overcome the bottleneck of traditional solar stills; machine learning analysis found thermal design to be the most significant parameter that contributes to high-efficiency solar evaporation.

What carries the argument

Micro/nanomaterials combined with thermal system configurations, analyzed via machine learning to rank factor importance for evaporation rate.

If this is right

  • Future solar still designs should prioritize thermal management features such as insulation, heat localization, and interface temperature control.
  • Material selection for solar evaporation should be evaluated first through its effect on thermal properties rather than other attributes.
  • Salt rejection and long-term durability become secondary targets once thermal design is optimized.
  • Investigations of solid-liquid interactions should focus on their influence on the phase-change process at the optimized thermal conditions.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If thermal design dominates, then hybrid systems that combine nanomaterials with passive thermal structures could be tested for cost reduction in off-grid settings.
  • The emphasis on interface temperature suggests experiments that directly measure and control vapor-layer conditions to isolate its contribution.
  • Extending the machine learning approach to include economic or scalability metrics could reveal trade-offs not captured in the current efficiency ranking.

Load-bearing premise

The reviewed papers and the data fed to the machine learning model form a representative, unbiased sample that would not change the ranking of thermal design if expanded.

What would settle it

A broader dataset or controlled experiment in which a non-thermal factor, such as material wettability or optical absorption, shows higher correlation with evaporation efficiency than thermal design parameters.

read the original abstract

In last decades, solar stills, as one of the solar desalination technologies, have been well studied in terms of their productivity, efficiency and economics. Recently, to overcome the bottleneck of traditional solar still, improving solar still by optimizing the solar evaporation process based on micro/nanomaterials have been proposed as a promising strategy. In this review, the recent development for achieving high-performance of solar still and solar evaporation are discussed, including materials as well as system configurations. Meanwhile, machine learning was used to analyze the importance of different factors on solar evaporation, where thermal design was founded to be the most significant parameter that contributes in high-efficiency solar evaporation. Moreover, several important points for the further investigations of solar still and solar evaporation were also discussed, including the temperature of the air-water interface, salt rejecting and durability, the effect of solid-liquid interaction on water phase change.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. This review paper discusses recent advances in using micro/nanomaterials to enhance the performance of solar stills and solar evaporation systems for desalination. It covers materials selection and system configurations aimed at overcoming limitations of traditional solar stills, applies machine learning to rank the importance of different factors influencing solar evaporation efficiency (concluding that thermal design is the most significant), and identifies future research priorities such as air-water interface temperature, salt rejection, durability, and solid-liquid interactions affecting phase change.

Significance. If the machine learning ranking of thermal design as the dominant factor is shown to rest on a transparent, bias-checked dataset drawn from the reviewed literature, the result could usefully direct experimental priorities in solar evaporation research. The compilation of material and configuration strategies may also serve as a reference for the field. However, the absence of methodological details on the ML component limits the ability to evaluate whether this ranking holds or generalizes.

major comments (1)
  1. [Abstract] Abstract (and the machine learning analysis section): The claim that machine learning analysis found thermal design to be the most significant parameter for high-efficiency solar evaporation lacks any description of literature inclusion/exclusion criteria, dataset size or composition, feature definitions, model type, training/validation procedure, or sensitivity checks. Without these, the reported ranking cannot be verified and remains vulnerable to selection biases in the underlying corpus of micro/nanomaterial studies.
minor comments (1)
  1. [Abstract] Typo: 'founded' should read 'found'.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive comments. The primary issue identified is the lack of methodological details for the machine learning analysis, which prevents verification of the reported ranking. We will address this through a major revision by expanding the relevant section with the requested information.

read point-by-point responses

  1. Referee: [Abstract] Abstract (and the machine learning analysis section): The claim that machine learning analysis found thermal design to be the most significant parameter for high-efficiency solar evaporation lacks any description of literature inclusion/exclusion criteria, dataset size or composition, feature definitions, model type, training/validation procedure, or sensitivity checks. Without these, the reported ranking cannot be verified and remains vulnerable to selection biases in the underlying corpus of micro/nanomaterial studies.

    Authors: We agree that the manuscript as submitted does not include these methodological details, which limits the ability to evaluate the ML results. In the revised manuscript we will add a dedicated subsection on the machine learning analysis that specifies: literature inclusion/exclusion criteria, dataset size and composition, precise feature definitions, the model type employed, training/validation procedures, and any sensitivity or robustness checks performed. This addition will allow readers to assess the transparency and potential biases of the analysis supporting the conclusion that thermal design is the dominant factor. revision: yes

Circularity Check

0 steps flagged

No significant circularity; review summarizes external literature with ML analysis as empirical ranking

full rationale

This is a review paper that collates developments from cited external studies on micro/nanomaterials for solar evaporation. The ML step ranks thermal design as the dominant factor based on a dataset drawn from the reviewed literature, but the paper presents this as an analysis result rather than a first-principles derivation or prediction that reduces to the inputs by construction. No self-definitional equations, fitted parameters renamed as independent predictions, load-bearing self-citations, or ansatz smuggling are present. The content remains self-contained against the external benchmarks of the cited papers, consistent with the default expectation for non-derivational reviews.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

As a review paper the work aggregates published results and performs a secondary ML analysis; it introduces no new free parameters, axioms, or invented physical entities beyond those already present in the reviewed literature.

pith-pipeline@v0.9.0 · 5703 in / 1134 out tokens · 29733 ms · 2026-05-25T19:17:55.407830+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

103 extracted references · 103 canonical work pages

  1. [1]

    Carrete, W

    J. Carrete, W. Li, N. Mingo, S. Wang, S. Curtarolo, Phys Rev X 2014, 4, 011019

    work page 2014

  2. [2]

    A. C. Davison, D. V. Hinkley, Bootstrap methods and their application , Cambridge university press, 1997

    work page 1997

  3. [3]

    Breiman, Mach Learn 2001, 45, 5

    L. Breiman, Mach Learn 2001, 45, 5

    work page 2001

  4. [4]

    Steinberg, P

    D. Steinberg, P. Colla, The top ten algorithms in data mining 2009, 9, 179

    work page 2009

  5. [5]

    X. Chen, M. Wang, H. Zhang, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2011, 1, 55

    work page 2011

  6. [6]

    Y. Zeng, J. Yao, B. A. Horri, K. Wang, Y. Wu, D. Li, H. Wang, Energ Environ Sci 2011, 4, 4074

    work page 2011

  7. [7]

    Y. Zeng, K. Wang, J. Yao, H. Wang, Chem Eng Sci 2014, 116, 704

    work page 2014

  8. [8]

    G. Ni, N. Miljkovic, H. Ghasemi, X. Huang, S. V. Boriskina, C. -T. Lin, J. Wang, Y. Xu, M. M. Rahman, T. Zhang, G. Chen, Nano Energy 2015, 17, 290

    work page 2015

  9. [9]

    D. Zhao, H. Duan, S. Yu, Y. Zhang, J. He, X. Quan, P. Tao, W. Shang, J. Wu, C. Song, T. Deng, Sci Rep 2015, 5, 17276

    work page 2015

  10. [10]

    Ishii, R

    S. Ishii, R. P. Sugavaneshwar, K. Chen, T. D. Dao, T. Nagao, Opt Mater Expr ess 2016, 6, 640

    work page 2016

  11. [11]

    Ishii, R

    S. Ishii, R. P. Sugavaneshwar, T. Nagao, The Journal of Physical Chemistry C 2016, 120, 2343

    work page 2016

  12. [12]

    H. Jin, G. Lin, L. Bai, A. Zeiny, D. Wen, Nano Energy 2016, 28, 397

    work page 2016

  13. [13]

    X. Wang, Y. He, G. Cheng, L. Shi, X. Liu, J. Zhu, Energ Convers Manage 2016, 130, 176

    work page 2016

  14. [14]

    X. Wang, G. Ou, N. Wang, H. Wu, ACS Appl Mater Interfaces 2016, 8, 9194

    work page 2016

  15. [15]

    M. S. Zielinski, J. W. Choi, T. La Grange, M. Modestino, S. M. Hashemi, Y. Pu, S. Birkhold, J. A. Hubbell, D. Psaltis, Nano Lett 2016, 16, 2159

    work page 2016

  16. [16]

    Amjad, G

    M. Amjad, G. Raza, Y. Xin, S. Pervaiz, J. Xu, X. Du, D. Wen, Appl Energ 2017, 206, 393

    work page 2017

  17. [17]

    Y. Fu, T. Mei, G. Wang, A. Guo, G. Dai, S. Wang, J. Wang, J. Li, X. Wang, Appl Therm Eng 2017, 114, 961

    work page 2017

  18. [18]

    H. Li, Y. He, Z. Liu, Y. Huang, B. Jiang, Appl Therm Eng 2017, 121, 617

    work page 2017

  19. [19]

    L. Shi, Y. He, Y. Huang, B. Jiang, Energ Convers Manage 2017, 149, 401

    work page 2017

  20. [20]

    X. Wang, Y. He, X. Liu, L. Shi, J. Zhu, Sol Energy 2017, 157, 35

    work page 2017

  21. [21]

    Z. Wang, Y. Liu, P. Tao, Q. Shen, N. Yi, F. Zhang, Q. Liu, C. Song, D. Zhang, W . Shang, T. Deng, Small 2014, 10, 3234

    work page 2014

  22. [22]

    K. Bae, G. Kang, S. K. Cho, W. Park, K. Kim, W. J. Padilla, Nat Commun 2015, 6, 10103

    work page 2015

  23. [23]

    Y. Ito, Y. Tanabe, J. Han, T. Fujita, K. Tanigaki, M. Chen, Adv Mater 2015, 27, 4302

    work page 2015

  24. [24]

    Y. Liu, S. Yu, R. Feng, A. B ernard, Y. Liu, Y. Zhang, H. Duan, W. Shang, P. Tao, C. Song, T. Deng, Adv Mater 2015, 27, 2768

    work page 2015

  25. [25]

    Zhang, B

    L. Zhang, B. Tang, J. Wu, R. Li, P. Wang, Adv Mater 2015, 27, 4889

    work page 2015

  26. [26]

    P. Fan, H. Wu, M. Zhong, H. Zhang, B. Bai, G. Jin, Nanoscale 2016, 8, 14617

    work page 2016

  27. [27]

    Z. Hua, B. Li, L. Li, X. Yin, K. Chen, W. Wang, The Journal of Physical Chemistry C 2016, 121, 60

    work page 2016

  28. [28]

    Y. Liu, J. Lou, M. Ni, C. Song, J. Wu, N. P. Dasgupta, P. Tao, W. Shang, T. Deng, ACS Appl Mater Interfaces 2016, 8, 772

    work page 2016

  29. [29]

    Y. Wang, L. Zhang, P. Wang, Acs Sustain Chem Eng 2016, 4, 1223. 51

    work page 2016

  30. [30]

    Zhang, C

    C. Zhang, C. Yan, Z. Xue, W. Yu, Y. Xie, T. Wang, Small 2016, 12, 5320

    work page 2016

  31. [31]

    L. Zhou, Y. Tan, D. Ji, B. Zhu, P. Zhang, J. Xu, Q. Gan, Z. Yu, J. Zhu, Sci. Adv. 2016, 2, e1501227

    work page 2016

  32. [32]

    L. Zhou, Y. Tan, J. Wang, W. Xu, Y. Yuan, W. Cai, S. Zhu, J. Zhu, Nat Photonics 2016, 10, 393

    work page 2016

  33. [33]

    G. Zhu, J. Xu, W. Zhao, F. Huang, ACS Appl Mater Interfaces 2016, 8, 31716

    work page 2016

  34. [34]

    C. Chen, Y. Li, J. Song, Z. Yang, Y. Kuang, E. Hitz, C. Jia, A. Gong, F. Jiang, J. Y. Zhu, B. Yang, J. Xie, L. Hu, Adv Mater 2017, 29

    work page 2017

  35. [35]

    D. Ding, W. Huang, C. Song, M. Yan, C. Guo, S. Liu, Chem Commun (Camb) 2017, 53, 6744

    work page 2017

  36. [36]

    X. Gao, H. Ren, J. Zhou, R. Du, C. Yin, R. Liu, H. Peng, L. Tong, Z. Liu, J. Zhang, Chem Mater 2017, 29, 5777

    work page 2017

  37. [37]

    Kashyap, A

    V. Kashyap, A. Al-Bayati, S. M. Sajadi, P. Irajizad, S. H. Wang, H. Ghasemi, J Mater Chem A 2017, 5, 15227

    work page 2017

  38. [38]

    C. Liu, J. Huang, C. -E. Hsiung, Y. Tian, J. Wang, Y. Han, A. Fratalocchi, Advanced Sustainable Systems 2017, 1, 1600013

    work page 2017

  39. [39]

    G. Wang, Y. Fu, X. Ma, W. Pi, D. Liu, X. Wang, Carbon 2017, 114, 117

    work page 2017

  40. [40]

    J. Wang, Z. Liu, X. Dong, C. -E. Hsiung, Y. Zhu, L. Liu, Y. Han, J Mater Chem A 2017, 5, 6860

    work page 2017

  41. [41]

    X. Wang, Y. He, X. Liu, G. Cheng, J. Zhu, Appl Energ 2017, 195, 414

    work page 2017

  42. [42]

    J. Yang, Y. Pang, W. Huang, S. K. Shaw, J. Schiffbauer, M. A. Pillers, X. Mu, S. Luo, T. Zhang, Y. Huang, G. Li, S. Ptasinska, M. Lieberman, T. Luo, Acs Nano 2017, 11, 5510

    work page 2017

  43. [43]

    L. Yi, S. Ci, S. Luo, P. Shao, Y. Hou, Z. Wen, Nano Energy 2017, 41, 600

    work page 2017

  44. [44]

    Z. Yin, H. Wang, M. Jian, Y. Li, K. Xia, M. Zhang, C. Wang, Q. Wang, M. Ma, Q. S. Zheng, Y. Zhang, ACS Appl Mater Interfaces 2017, 9, 28596

    work page 2017

  45. [45]

    Zhang, J

    P. Zhang, J. Li, L. Lv, Y. Zhao, L. Qu, Acs Nano 2017, 11, 5087

    work page 2017

  46. [46]

    T. F. Chala, C. M. Wu, M. H. Chou, Z. L. Guo, ACS Appl Mater Interfaces 2018, 10, 28955

    work page 2018

  47. [47]

    M. Chen, Y. Wu, W. Song, Y. Mo, X. Lin, Q. He, B. Guo, Nanoscale 2018, 10, 6186

    work page 2018

  48. [48]

    F. Tao, Y. Zhang, K. Yin, S. Cao, X. Chang, Y. Lei, D. S. Wang, R. Fan, L. Dong, Y. Yin, X. Chen, ACS Appl Mater Interfaces 2018, DOI: 10.1021/acsami.8b11786

    work page doi:10.1021/acsami.8b11786 2018

  49. [49]

    W. Xu, X. Hu, S. Zhuang, Y. Wang, X. Li, L. Zhou, S. Zhu, J. Zhu, Adv Energy Mater 2018, 8, 1702884

    work page 2018

  50. [50]

    X. Yang, Y. Yang, L. Fu, M. Zou, Z. Li, A. Cao, Q. Yuan, Adv Funct Mater 2018, 28, 1704505

    work page 2018

  51. [51]

    Gh asemi, G

    H. Gh asemi, G. Ni, A. M. Marconnet, J. Loomis, S. Yerci, N. Miljkovic, G. Chen, Nat Commun 2014, 5, 4449

    work page 2014

  52. [52]

    F. M. Canbazoglu, B. Fan, A. Kargar, K. Vemuri, P. R. Bandaru, Aip Adv 2016, 6, 085218

    work page 2016

  53. [53]

    Jiang, L

    Q. Jiang, L. Tian, K. K. Liu, S. Tadepalli, R. Raliya, P. Biswas, R. R. Naik, S. Singamaneni, Adv Mater 2016, 28, 9400

    work page 2016

  54. [54]

    X. Li, W. Xu, M. Tang, L. Zhou, B. Zhu, S. Zhu, J. Zhu, Proc Natl Acad Sci U S A 2016, 113, 13953

    work page 2016

  55. [55]

    G. Ni, G. Li, Svetlana V. Boriskina, H. Li, W. Yang, T. Zhang, G. Chen, Nature Energy 2016, 1, 16126. 52

    work page 2016

  56. [56]

    S. M. Sajadi, N. Farokhnia, P. Irajizad, M. Hasnain, H. Ghasemi, J Mater Chem A 2016, 4, 4700

    work page 2016

  57. [57]

    L. Tian, J. Luan, K. K. Liu, Q. Jiang, S. Tadepalli, M. K. Gupta, R. R. Naik, S. Singamaneni, Nano Lett 2016, 16, 609

    work page 2016

  58. [58]

    Y. Fu, G. Wang, T. Mei, J. Li, J. Wang, X. Wang, Acs Sustain Chem Eng 2017, 5, 4665

    work page 2017

  59. [59]

    X. Hu, W. Xu, L. Zhou, Y. Tan, Y. Wang, S. Zhu, J. Zhu, Adv Mater 2017, 29

    work page 2017

  60. [60]

    C. Jia, Y. Li, Z. Yang, G. Chen, Y. Yao, F. Jiang, Y. Kuang, G. Pastel, H. Xie, B. Yang, S. Das, L. Hu, Joule 2017, 1, 588

    work page 2017

  61. [61]

    Jiang, H

    Q. Jiang, H. Gholami Derami, D. Ghim, S. Cao, Y.-S. Jun, S. Singamaneni, J Mater Chem A 2017, 5, 18397

    work page 2017

  62. [62]

    M. Kaur, S. Ishii, S. L. Shinde, T. Nagao, Acs Sustain Chem Eng 2017, 5, 8523

    work page 2017

  63. [63]

    R. Li, L. Zhang, L. Shi, P. Wang, Acs Nano 2017, 11, 3752

    work page 2017

  64. [64]

    Y. Li, T. Gao, Z. Yang, C. Chen, Y. Kuang, J. Song, C. Jia, E. M. Hitz, B. Yang, L. Hu, Nano Energy 2017, 41, 201

    work page 2017

  65. [65]

    Y. Li, T. Gao, Z. Yang, C. Chen, W. Luo, J. Song, E. Hitz, C. Jia, Y. Zhou, B. Liu, B. Yang, L. Hu, Adv Mater 2017, 29

    work page 2017

  66. [66]

    K. K. Liu, Q. Jiang, S. Tadepalli, R. Raliya, P. Biswas, R. R. Naik, S. Singamaneni, ACS Appl Mater Interfaces 2017, 9, 7675

    work page 2017

  67. [67]

    Z. Liu, H. Song, D. Ji, C. Li, A. Cheney, Y. Liu, N. Zhang, X. Zeng, B. Chen, J. Gao, Y. Li, X. Liu, D. Aga, S. Jiang, Z. Yu, Q. Gan, Global Chall 2017, 1, 1600003

    work page 2017

  68. [68]

    Z. Liu, Z. Yang, X. Huang, C. Xuan, J. Xie, H. Fu, Q. Wu, J. Zhang, X. Zhou, Y. Liu, J Mater Chem A 2017, 5, 20044

    work page 2017

  69. [69]

    S. Ma, C. P. Chiu, Y. Zhu, C. Y. Tang, H. Long, W. Qarony, X. Zhao, X. Zhang, W. H. Lo, Y. H. Tsang, Appl Energ 2017, 206, 63

    work page 2017

  70. [70]

    H. Ren, M. Tang, B. Guan, K. Wang, J. Yang, F. Wang, M. Wang, J. Shan, Z. Chen, D. Wei, H. Peng, Z. Liu, Adv Mater 2017, 29, 1702590

    work page 2017

  71. [71]

    L. Shi, Y. Wang, L. Zhang, P. Wang, J Mater Chem A 2017, 5, 16212

    work page 2017

  72. [72]

    G. Wang, Y. Fu, A. Guo, T. Mei, J. Wang, J. Li, X. Wang, Chem Mater 2017, 29, 5629

    work page 2017

  73. [73]

    Z. Wang, Q. Ye, X. Liang, J. Xu, C. Chang, C. Song, W. Shang, J. Wu, P. Tao, T. Deng, J Mater Chem A 2017, 5, 16359

    work page 2017

  74. [74]

    X. Wu, G. Y. Chen, W. Z hang, X. Liu, H. Xu, Advanced Sustainable Systems 2017, 1, 1700046

    work page 2017

  75. [75]

    N. Xu, X. Hu, W. Xu, X. Li, L. Zhou, S. Zhu, J. Zhu, Adv Mater 2017, 29

    work page 2017

  76. [76]

    G. Xue, K. Liu, Q. Chen, P. Yang, J. Li, T. Ding, J. Duan, B. Qi, J. Zhou, ACS Appl Mater Interfaces 2017, 9, 15052

    work page 2017

  77. [77]

    J. D. Yao, Z. Q. Zheng, G. W. Yang, Nanoscale 2017, 9, 16396

    work page 2017

  78. [78]

    M. Zhu, Y. Li, G. Chen, F. Jiang, Z. Yang, X. Luo, Y. Wang, S. D. Lacey, J. Dai, C. Wang, C. Jia, J. Wan, Y. Yao, A. Gong, B. Yang, Z. Yu, S. Das, L. Hu, Adv Mater 2017, 29

    work page 2017

  79. [79]

    Q. Chen, Z. Pei, Y. Xu, Z. Li, Y. Yang, Y. Wei, Y. Ji, Chem Sci 2018, 9, 623

    work page 2018

  80. [80]

    J. Fang, J. Liu, J. Gu, Q. Liu, W. Zhang, H. Su, D. Zhang, Chem Mater 2018, 30, 6217

    work page 2018

Showing first 80 references.